Vision2025~120 minarXiv:2512.17504

InsertAnywhere: Bridging 4D Scene Geometry and Diffusion Models for Realistic Video Object Insertion

score 2Hoiyeong Jin, Hyojin Jang, Jeongho Kim +6KAIST AI↑ 97★ 82Dec 19, 2025cs.CVhttps://github.com/myyzzzoooo/InsertAnywhere

🎯 Pitch

Inserting an object into a video so it realistically moves, occludes, and casts correct shadows without leaking into the background has been a major unsolved challenge—diffusion models alone can't do it. InsertAnywhere cracks this by first reconstructing the scene in 4D to generate geometrically flawless, occlusion-aware masks across all frames, then using a diffusion model that was explicitly taught to harmonize lighting and reflections around the inserted object.

1. Executive Summary

This paper introduces InsertAnywhere, a two-stage framework for realistic video object insertion that combines 4D scene geometry with diffusion-based video generation. Evaluated on the authors' proposed VOIBench dataset using Wan2.1-VACE-14B as the base video model, the framework pairs a 4D-aware mask generation module (propagating user-specified object placement across frames via 4D reconstruction and scene flow, handling occlusion and camera motion) with a video inpainting model fine-tuned on ROSE++ (a synthetic dataset that inverts object removal into insertion with VLM-generated reference images, enabling the model to synthesize illumination and shadow variations around the inserted object). InsertAnywhere achieves CLIP-I of 0.8122 and DINO-I of 0.5678 on subject consistency, substantially outperforming commercial tools Pika-Pro and Kling, with the ablation demonstrating that each component — 4D-aware masks, first-frame inpainting, and ROSE++ fine-tuning — provides measurable gains over simpler mask-and-inpaint baselines. The user study confirms strong human preference (79% for object realism, 87% for occlusion integrity), establishing that geometrically consistent object insertion with photorealistic local lighting effects is achievable through explicit 4D scene representation, but only when the mask generation accounts for both camera motion and dynamic scene interactions via scene flow.

2. Context and Motivation

The Core Problem: Video Object Insertion Is Not Just a Video Inpainting Problem

This paper tackles Video Object Insertion (VOI) — the task of taking an existing video, a reference image of a new object, and a user-specified placement, then generating a new video where that object appears naturally integrated into every frame. At first glance, this sounds like a straightforward extension of video inpainting: you have a mask, you fill it with the object, and you're done. But the paper argues that the VOI problem is fundamentally harder than inpainting, and that treating it as "inpainting with a reference image" misses two critical requirements that separate a workable demo from commercial-quality output.

The first missing requirement is 4D-aware placement. A reference object image provides no information about what scale, orientation, or perspective the object should have in any given frame. In the first frame, the user can specify these things manually — but that only solves the problem for one moment in time. Across the remaining frames, the object must move consistently with the camera trajectory (growing larger as the camera moves closer, shifting perspective as the viewpoint changes) and must correctly handle occlusions with dynamically moving scene elements. This requires genuine 4D understanding — knowing where the object sits in 3D space and how that 3D position projects onto each frame's 2D image plane given the camera motion. Prior methods largely sidestep this: they either require masks for every frame (impractical for users), operate on 2D representations that cannot handle parallax and perspective shifts, or use mask-free generation that sacrifices precise user control over object placement and scale.

The second missing requirement is illumination-aware synthesis. When you insert a real object into a real scene, the object doesn't just appear in isolation — it interacts with the scene's lighting. It casts shadows on nearby surfaces. Its brightness changes when a door opens and sunlight floods in. Its surface reflects the color of adjacent objects. Yet most video inpainting models are trained by masking out some region and learning to fill it in using surrounding pixels. This training paradigm provides zero explicit supervision for lighting effects that extend outside the mask boundary. A model trained this way can learn to copy-paste the reference object into the masked area, but it won't know that the object should cast a shadow stretching across the floor. That shadow exists outside the mask, and the training signal never penalizes its absence.

The paper's central insight is that these two requirements — 4D placement and illumination synthesis — are orthogonal and both are necessary for commercial-quality VOI. Having one without the other produces videos that look wrong in different ways. Perfect placement with no lighting effects looks like a photoshopped cutout. Beautiful lighting effects applied to a badly-placed object creates a realistic-looking object floating in midair or penetrating scene geometry. The paper's two-stage architecture — a 4D-aware mask generator followed by an illumination-aware video synthesis model — directly reflects this problem decomposition.

Why This Problem Matters

The paper frames VOI as having immediate commercial importance, specifically for virtual product placement and branded content generation (Section 1, Section 5). In traditional advertising, inserting a product into an existing video — say, placing a branded soda can on a table in a lifestyle video — requires either physically filming with the product present (expensive, logistically complex) or manually compositing it frame-by-frame in post-production (extremely labor-intensive). A VOI system that works reliably would let advertisers take any existing video and insert products after the fact, adjusting placement, scale, and object identity in software. This is not merely a cost reduction; it opens new possibilities for dynamic ad insertion, A/B testing product placements, or localizing brand content for different markets.

But the significance goes beyond advertising. Any video editing workflow that involves adding objects to existing footage — film VFX, architectural visualization, augmented reality content — could benefit from automated VOI that handles camera motion, occlusions, and lighting automatically rather than requiring manual keyframing and compositing per frame. The paper's user study (Table 4 in supplementary) confirms that this matters to viewers: on occlusion integrity, their method received 86.67% of user preferences versus 13.33% for Kling and 0% for Pika-Pro. Users can clearly tell when an inserted object doesn't respect scene geometry.

There is also a methodological significance to this work. The paper demonstrates that explicit 4D scene reconstruction — building an actual 3D representation from a monocular video using pretrained depth, flow, and segmentation models — can be productively combined with 2D diffusion-based generation. This bridges two communities that often work in isolation: the 3D/4D reconstruction community and the generative video editing community. The key finding is that the 4D representation doesn't need to be perfect to be useful; it only needs to provide accurate enough masks and camera alignment to give the video generation model the right spatial conditioning. The generation model handles the rest through its learned visual priors.

Limitations of Prior Approaches

The paper identifies three categories of existing work, each with specific failure modes that InsertAnywhere is designed to address.

Category 1: Inpainting-Based Methods (Mask-Constrained Editing)

This includes research works like INVI [21], VideoAnyDoor [25], and DreamInsert [32], as well as the training paradigm used by most video inpainting models. These methods take a mask that specifies where the object should appear and regenerate only the pixels within that mask, leaving everything outside untouched.

Where they fall short: The paper identifies two distinct failures. First, the mask can only cover the object region (Section 3.1). If you mask only the object, the model has no way to generate shadows or lighting changes that extend beyond the object boundary. The inserted object looks unnaturally detached from the scene — it's geometrically correct but photometrically isolated. The ablation study makes this concrete: Figure 6(b) shows that without ROSE++ fine-tuning, the model produces no shadow beyond the object mask, resulting in behavior "similar to a simple inpainting model." Second, if you try to fix this by using a larger mask that covers the expected shadow region, you sacrifice user controllability — the model starts regenerating large portions of the scene, potentially altering content the user wanted to preserve. This tradeoff between lighting realism and scene preservation is inherent to mask-based approaches and cannot be resolved by better inpainting models alone.

A deeper issue: inpainting models are typically trained via self-supervised reconstruction — mask out random regions of a video, train the model to fill them in using surrounding context. This teaches the model to reconstruct what was there, not to synthesize what should be there given a new object. The model never sees examples where an inserted object produces shadows, so it never learns to generate them. The paper is explicit about this (Section 3.4):

"Previous datasets relied primarily on self-supervised inpainting training with mask-based reconstruction, where models learn to fill masked regions using surrounding pixels. Such training, however, provides no explicit supervision for lighting or shadow consistency, making it impossible for models to capture illumination changes caused by newly inserted objects."

Category 2: Propagation-Based Methods

GenProp [14] takes a different approach: the user edits the first frame (inserting the object with whatever image editing tool they prefer), and GenProp propagates the appearance changes from that first frame through the rest of the video. It uses a Region-Aware Loss that encourages the model to preserve changes made in the first frame while maintaining temporal coherence.

Where it falls short (Section 2): The critical failure is that GenProp "does not explicitly account for visibility changes over time." The appearance of the inserted object in frame 1 — its exact pixel colors and spatial extent — is propagated assuming it will remain visible and unoccluded. When the object becomes partially occluded (e.g., a person walks in front of it) and then reappears, GenProp has no mechanism to handle the disappearance and reappearance. The propagated appearance from frame 1 doesn't contain information about which pixels should be visible when, so occlusions produce artifacts: the object might show through the occluder, or disappear entirely, or reappear with corrupted geometry.

The paper's example (Section 4.2, Figure 4) illustrates this with a pepper shaker moving behind a hand: "Kling fails to handle the occlusion of the pepper shaker moving from the third to the fourth column, where it appears in front of the hand before moving behind it." While this example specifically critiques Kling, the underlying failure — lack of explicit occlusion reasoning — is what GenProp suffers from as well.

Category 3: Commercial Text-Guided Video Generation Tools

Tools like Pika Pro [18] and Kling [23] represent the current state of commercially available VOI capability. Users provide a text prompt describing where to place the object (along with a reference image), and the tool generates the edited video.

Where they fall short: The paper's experiments reveal systematic failures that point to a fundamental limitation: text prompts cannot precisely convey 4D spatial constraints. Across the qualitative examples (Figures 4, 9, 10, 11, 12, 13 in the main paper and supplementary), the baselines exhibit three recurring problems:

Object identity drift: The generated object doesn't match the reference. In Figure 4 (top), the reference is a drawer with three compartments, but Kling generates one with four compartments, and Pika-Pro produces one with "noticeable color difference." Quantitatively, Table 1 shows Pika-Pro's CLIP-I is 0.4940 and DINO-I is 0.3856 — substantially below InsertAnywhere's 0.8122 and 0.5678, indicating poor subject fidelity.
Object swapping: The model removes existing content and replaces it with the reference object rather than inserting the object into empty space. In Figure 9 (supplementary), Kling "often performs object swapping rather than inserting the target object, leading to the removal of existing items or people." This happens because text-guided models lack explicit spatial masks telling them which region should contain the object and which should be preserved.
Physically impossible placement: Despite explicit spatial instructions in the prompt, Kling "places the object in physically inconsistent regions" (supplementary text, Figure 10). The model doesn't understand 3D scene geometry, so it cannot reliably determine where a flat surface exists to place an object.
Occlusion failures: As with the pepper shaker example, objects fail to correctly pass behind foreground elements. The supplementary text summarizes: "They lack the spatial reasoning ability required to correctly interpret scene geometry and object–scene relationships."

These failures are not implementation bugs — they are symptoms of using language as the interface for a task that inherently requires precise spatial coordinates, 3D understanding, and per-pixel control. Language is fuzzy and ambiguous about geometry; even a perfectly phrased prompt like "place the object on the table in front of the mirror" doesn't specify where on the table, at what scale, or at what orientation relative to the camera.

The Gap the Paper Identifies

Synthesizing these critiques, the paper identifies a clear gap in the landscape (Section 3.1):

Inpainting models offer precise spatial control (masks) but cannot synthesize lighting effects extending beyond mask boundaries and are trained without explicit insertion supervision.
Propagation methods maintain appearance consistency but fail under occlusion because they lack 4D understanding of visibility changes.
Commercial text-guided tools are easy to use but sacrifice spatial precision and object fidelity, producing videos where the object identity, placement, or scene integrity is compromised.

No existing system simultaneously delivers: (1) precise user control over object placement and scale, (2) geometrically consistent propagation across frames under camera motion and occlusions, and (3) photometrically realistic synthesis of lighting effects and shadows around the inserted object. InsertAnywhere is designed to fill exactly this gap.

How This Paper Positions Itself

InsertAnywhere positions itself not as an incremental improvement to any one of the three categories above, but as a novel architecture that decomposes VOI into orthogonal subproblems solved by different mechanisms, with each mechanism chosen to handle a specific failure mode of prior work.

The decomposition strategy. The paper's key architectural decision is to separate VOI into two stages (Figure 2): a mask generation stage that solves the 4D placement problem, and a video synthesis stage that solves the illumination-aware generation problem. This is not an arbitrary split — it directly mirrors the two requirements identified as missing from prior work. The mask generator handles "where should the object be and what should it occlude?" (a geometric question), while the video synthesis model handles "given the placement, what should the video look like?" (an appearance question).

This decomposition is important because it allows each stage to be solved with the most appropriate tool. The mask generator uses explicit 4D reconstruction (depth estimation, camera pose recovery, scene flow) — geometric reasoning that diffusion models are not inherently good at. The video synthesis model uses a fine-tuned diffusion model — appearance synthesis that explicit geometry models cannot do. The interface between them is the mask sequence: a per-frame, per-pixel specification of which pixels belong to the inserted object. This is a clean, minimal interface that encodes all the geometric information the synthesis model needs without constraining how it generates appearance.

Leveraging image model priors via first-frame inpainting. A subtle but important design choice: rather than having the video model generate the object's appearance from scratch, the paper uses an image inpainting model to generate the first frame with high fidelity (Section 3.4). This first frame serves as an "anchor" — it establishes the object's exact appearance, color, texture, and lighting in the initial viewpoint. The video model's job is then to propagate this established appearance through the remaining frames, maintaining consistency as the viewpoint changes. The paper invokes this as an explicit strategy (Section 3.4):

"Since current image inpainting models [31] demonstrate stronger visual reconstruction capability than video models, the first frame is generated with high fidelity to serve as a reliable visual reference. The visual information from this frame is then effectively propagated throughout the subsequent video generation process."

This is a practical acknowledgement that image models currently outperform video models on per-frame quality, and it mirrors strategies used in other video editing works (the paper cites [5], Keyframe-guided creative video inpainting).

ROSE++ as enabling training with explicit insertion supervision. Perhaps the paper's most important positioning move is the construction of the ROSE++ dataset (Section 3.3). Prior work was constrained by the lack of supervised VOI training data — you need triples of (video without object, video with object, reference object image) to train a model to perform insertion, and such data is extremely rare. The paper's key insight is to invert the ROSE dataset [15] (designed for object removal, providing pairs of object-present and object-removed videos) into an insertion dataset by swapping which is input and which is target, and then using a VLM to generate the missing reference object images.

This inversion is not merely a data augmentation trick. It means that during training, the model sees examples where the "source" video has no object and no shadows, and the "target" video has the object and its associated lighting effects (shadows, reflections, illumination changes). By learning to map from source to target, the model implicitly learns that inserting an object requires generating lighting effects beyond the object mask. This is supervised training for exactly the capability that self-supervised inpainting cannot provide. The paper is explicit about this (Section 3.4):

"ROSE++ augments each object-removal pair into a triplet by adding a corresponding object image generated via a vision–language model, enabling consistent observation of geometric and illumination relationships. This allows the model to learn not only to restore spatial alignment but also to synthesize soft shadows, lighting variations, and material-dependent shading."

What makes this paper different from prior work. The paper's positioning can be summarized as follows:

Against inpainting methods: InsertAnywhere doesn't just inpaint inside a mask; it synthesizes lighting effects beyond the mask boundary, trained explicitly to do so via ROSE++.
Against propagation methods: InsertAnywhere doesn't naively propagate 2D appearance; it uses 4D reconstruction to handle occlusions and visibility changes correctly, generating masks that respect scene geometry rather than assuming continuous visibility.
Against commercial tools: InsertAnywhere doesn't rely on text prompts for spatial control; it provides a GUI for precise 3D placement in the first frame and uses 4D reconstruction to propagate that placement geometrically, achieving spatial accuracy commercial tools cannot match.
Against all prior work: InsertAnywhere treats VOI as requiring two orthogonal capabilities (4D placement + illumination synthesis) and solves them with separate, specialized modules rather than hoping a single end-to-end model will somehow learn both from insufficient training data. The ROSE++ dataset provides the supervised training signal that makes the illumination synthesis module possible, filling a data gap that previously forced methods to rely on self-supervised inpainting paradigms incapable of learning lighting effects.

3. Technical Approach

3.1 Reader Orientation

InsertAnywhere is a two-stage video editing system that takes an existing video, a reference image of a new object, and a user's placement specification, then produces a new video where that object appears naturally integrated into the scene throughout all frames. The system solves the problem of realistic video object insertion by decomposing it into two orthogonal subproblems: first, determining where the object should appear in 3D space across all frames (solved by explicit 4D scene reconstruction and geometric mask propagation), and second, generating what the video should look like given those masks, including both the object itself and the lighting effects it introduces (solved by a fine-tuned diffusion-based video generation model trained on a purpose-built synthetic dataset of insertion examples).

3.2 Big-Picture Architecture (Diagram in Words)

The system has two major stages connected by a mask sequence interface:

4D-Aware Mask Generation Module (Section 3.2): Takes the source video, a reference object image, and user-specified placement in the first frame. Reconstructs the scene into a 4D representation (3D geometry + time), converts the reference object into a 3D point cloud, places it in the reconstructed scene at the user-specified location, propagates its position across frames using scene flow, and reprojects it onto each frame to produce a temporally consistent binary mask sequence. The output is a per-frame mask telling the synthesis module exactly which pixels belong to the inserted object.
Video Object Insertion Module (Section 3.4): Takes the source video, the reference object image, and the generated mask sequence. Uses a pretrained image inpainting model to generate a high-fidelity first frame with the object inserted, then feeds this frame as context to a diffusion-based video generation model (fine-tuned with LoRA on the ROSE++ dataset) that synthesizes the remaining frames, producing both the inserted object and its associated lighting effects (shadows, illumination changes) in areas both inside and outside the mask boundaries.

Between these two stages sits the ROSE++ Dataset (Section 3.3) — a synthetic training dataset that enables supervised learning of illumination-aware insertion by inverting an object-removal dataset and using a VLM to generate missing reference object images.

Information flows as follows: source video and reference object → user places object in 3D GUI → 4D reconstruction determines scene geometry → mask sequence generated via projection → first frame synthesized by image inpainting model → remaining frames synthesized by fine-tuned video diffusion model → final video with object inserted and lighting effects rendered.

3.3 Roadmap for the Deep Dive

First, the 4D scene reconstruction pipeline — how monocular video is converted into a coherent 3D + time representation using pretrained vision models — because the mask generation module depends on this representation.
Second, user-controlled object placement and conversion to a 3D point cloud, since this establishes the initial spatial configuration that will be propagated temporally.
Third, scene-flow-based object propagation — the mechanism for updating the object's 3D position across frames to handle dynamic scene interactions — because this is what distinguishes 4D-aware from naive static placement.
Fourth, camera-aligned reprojection and mask extraction, since this converts the 4D representation into the per-frame mask sequence that serves as the interface between the two stages.
Fifth, the ROSE++ dataset construction — the VLM-based object retrieval process and why it prevents copy-paste artifacts — because the video synthesis model's training depends on it.
Sixth, the video object insertion model — first-frame inpainting, LoRA fine-tuning on ROSE++, and how the model learns to synthesize lighting effects beyond mask boundaries — since this is the appearance-generation stage that produces the final output.

3.4 Detailed, Sentence-Based Technical Breakdown

This is primarily a systems paper whose core idea is that video object insertion requires two orthogonal capabilities — geometrically consistent 4D placement and illumination-aware appearance synthesis — and that these can be achieved by decomposing the problem into an explicit 4D reconstruction pipeline followed by a diffusion-based generation model trained with supervised insertion examples from an inverted object-removal dataset.

4D Scene Reconstruction from Monocular Video

The mask generation module begins by reconstructing the input video into a temporally consistent 4D representation — meaning that for every frame, there is a 3D understanding of the scene geometry, and these 3D representations are aligned across frames to form a coherent 4D (3D + time) model. The paper builds on Uni4D [30], a paradigm that orchestrates multiple pretrained vision models rather than training a dedicated 4D understanding network from scratch.

What Uni4D provides. Uni4D [30] demonstrated that a single video can be converted into a temporally consistent 4D representation by integrating predictions from several pretrained models:

Depth estimation [17] provides per-frame metric depth maps — for each pixel in each frame, an estimate of how far that surface is from the camera. This converts 2D pixels into 3D points in the camera's coordinate system.
Optical flow [10] provides dense pixel correspondences between consecutive frames — for each pixel in frame t, a 2D vector indicating where that surface point moved in frame t+1. This enables establishing temporal correspondence without explicit 3D reasoning.
Camera pose recovery estimates the camera's rotation and translation between frames — where the camera was pointing and where it was located — which is essential for projecting 3D points back into 2D image coordinates consistently across the sequence.
Segmentation models [4, 11, 13] identify which pixels belong to which objects or semantic regions, enabling the reconstruction to reason about distinct scene elements rather than treating the scene as an undifferentiated point cloud.

The key insight behind Uni4D is that these models were trained independently on different tasks and datasets, but their outputs can be combined through geometric constraints to produce a coherent 4D representation. For example, a point tracked by optical flow from frame t to frame t+1 should, when lifted to 3D using the depth estimate at frame t, project back to the corresponding 2D location in frame t+1 given the estimated camera motion. Violations of this constraint indicate errors in one or more of the component models, and the system can optimize for consistency.

Why this approach over training a single end-to-end 4D reconstruction model. The paper does not train a new 4D reconstruction network; it inherits Uni4D's existing pipeline. This is a practical engineering choice: pretraining a 4D reconstruction model from scratch requires massive multi-view video datasets with ground-truth geometry, which are scarce. The foundation-model-based approach leverages models that were trained on much larger datasets (e.g., Segment Anything was trained on millions of images with segmentation annotations) and transfers their capabilities to the 4D reconstruction task through geometric consistency constraints. The tradeoff is that the reconstruction quality is bounded by the weakest component model — if depth estimation fails on a particular scene, the 4D representation will be degraded, and downstream mask propagation will suffer. The paper does not provide quantitative evaluations of reconstruction accuracy, which is a limitation acknowledged implicitly by the fact that failure cases in mask generation would propagate to the final video quality.

What the reconstruction produces in operational terms. At the end of this stage, the system has:

A 3D point cloud for each frame (pixels lifted to 3D via depth estimates).
Camera pose estimates (rotation and translation) for each frame relative to a common coordinate system.
Temporal correspondences between points across frames (via optical flow and scene flow).

This is sufficient to place an object in the first frame's 3D coordinate system and track where it should appear in all subsequent frames, accounting for both camera motion and the object's own movement through the scene.

User-Controlled Object Placement

Once the 4D scene representation is built, the user must specify where the reference object should appear. The paper provides a GUI-based placement interface (visible in Figure 2, bottom-left panel) that operates in the reconstructed 3D space of the first frame.

Input representation of the reference object. The reference object image I_obj is a 2D image showing the object (typically on a white background, as generated by the VLM retrieval process described in Section 3.3). A single 2D image is insufficient for 3D placement because it provides no depth or volumetric information. The paper converts this 2D image into a 3D point cloud using a pretrained single-view reconstruction network [29]:

The reference image is fed through a pretrained single-view 3D reconstruction model that estimates the object's 3D shape, producing a point cloud:

$Y = \{ y_j \in \mathbb{R}^3 \}$

where each $y_j$ is a 3D point representing a location on the object's surface, expressed in a local coordinate system centered on the object. The paper uses the model from Xiang et al. [29], which generates structured 3D latents for scalable 3D generation — this model was pretrained on large-scale 3D object datasets and can estimate plausible 3D geometry from a single view, though it necessarily hallucinates unseen sides of the object.

Rigid transformation to scene coordinates. The point cloud $Y$ is defined in its own local coordinate system (e.g., with the object centered at the origin). To place it in the reconstructed scene, the system applies a rigid (similarity) transformation:

$y'_j = s_{obj} R_{obj} \, y_j + t_{obj}$

where:

$R_{obj} \in SO(3)$ is a 3×3 rotation matrix that orients the object in the scene (controlling which direction it faces),
$t_{obj} \in \mathbb{R}^3$ is a 3D translation vector that positions the object's centroid relative to the scene origin,
$s_{obj} \in \mathbb{R}^+$ is a global scale factor that uniformly resizes the object (preserving aspect ratios).

What this computes. The transformation takes each point $y_j$ in the object's local frame, rotates it by $R_{obj}$ , scales it by $s_{obj}$ , and translates it by $t_{obj}$ to produce $y'_j$ — the point's position in the scene's world coordinate system. The result is a 3D point cloud $\{y'_j\}$ representing the object's surface geometry positioned and oriented within the reconstructed scene at the user-specified location.

Why this form. The transformation is a similarity transform (rotation + translation + uniform scaling) rather than a general affine transform (which could include shearing or non-uniform scaling). This constraint preserves the object's 3D shape — a sphere remains a sphere, just at a different size and location. Non-uniform scaling would distort the object (e.g., stretching it into an ellipsoid), which would violate the user's expectation that the inserted object resembles the reference image. The rotation is specified in $SO(3)$ (the space of proper rotations, excluding reflections) because physical objects cannot be mirror-reflected by rigid motion. The uniform scale factor encodes the real-world size of the object — a critical parameter because the reference image alone provides no absolute scale information (a 2D image of a chair could represent a dollhouse chair or a full-size chair). The user must specify this scale through the GUI, implicitly setting the object's size relative to the known scene geometry.

The user interface. The paper's GUI (Figure 2) allows the user to interactively adjust $R_{obj}$ , $t_{obj}$ , and $s_{obj}$ while viewing the reconstructed 3D scene. The user navigates the 3D space, places the object point cloud at the desired location, rotates it to the desired orientation, and scales it to match the scene context. This is possible because the first frame's 3D reconstruction provides visual reference — the user can see the table surface, floor plane, and other geometric features, and can precisely position the object relative to these known structures. This is a fundamentally different interaction paradigm from the text-prompt approach used by commercial tools, where spatial precision is limited by the ambiguity of natural language.

Scene Flow Based Object Propagation

With the object placed in the first frame's 3D coordinate system, the next challenge is to determine where it should appear in all subsequent frames. A naive approach would be to keep the object fixed at its initial 3D position — meaning that if the camera moves, the object's projection onto the image plane changes correctly due to perspective, but the object itself does not move through the world. The paper argues that this assumption often fails (Section 3.2.3):

"For instance, if an apple rests on a plate and a person lifts the plate, the apple should naturally move along with it."

In other words, if the object is placed on a moving surface (a cart, a table being pushed, a person's hand), it should track that surface's motion. The object's absolute position in world coordinates is not constant — it moves because the supporting surface moves.

Estimating local scene flow. To capture this dynamic behavior, the system computes the scene flow — the 3D motion field — around the inserted object. The process works as follows:

Optical flow estimation: The system first computes the dense optical flow between consecutive frames using SEA-RAFT [26]. For each pixel in frame t, SEA-RAFT produces a 2D vector $V_{t \to t+1}$ indicating that pixel's displacement to frame t+1. SEA-RAFT is chosen for its balance of accuracy and efficiency (it was designed to be simpler and faster than prior RAFT variants while maintaining competitive accuracy). The optical flow is a 2D field — it captures apparent motion on the image plane, not true 3D motion.
Identifying nearby scene points: In the first frame, the system identifies the $K$ nearest 3D points in the reconstructed scene to the inserted object's position. These are points on the surrounding scene geometry — the table surface, the floor, nearby objects — that the inserted object is "attached" to or resting on. The paper does not specify the exact value of $K$ ; it is described as a parameter for identifying a local neighborhood.
Projecting to 2D: These $K$ nearest 3D points are projected onto the 2D image plane of the first frame using the camera intrinsics and extrinsics. This gives a set of 2D pixel locations corresponding to the scene geometry near the object.
Matching to optical flow: Each of these 2D locations is matched with the starting location of the optical flow vector $V_{t \to t+1}$ — that is, for each nearby scene pixel in frame t, the system looks up where SEA-RAFT says that pixel moved in frame t+1.
Lifting back to 3D: The 2D flow vectors (displacements on the image plane) are lifted back into 3D using the depth estimates for both frames. A 2D displacement $(\Delta u, \Delta v)$ combined with the depth at the starting point and the camera geometry yields a 3D motion vector. This produces a set of 3D motion vectors — one for each of the $K$ nearby points — that approximate the local scene flow: how the surfaces near the object moved in 3D between frames.

Updating the object position. The system computes the average of these $K$ 3D motion vectors and uses it to update the object's position:

$y'_{j,t+1} = y'_{j,t} + \bar{V}_{t \to t+1}(y'_{j,t})$

where $y'_{j,t}$ is the 3D position of point $j$ on the object at frame $t$ , and $\bar{V}_{t \to t+1}(y'_{j,t})$ is the averaged scene flow vector estimated from the $K$ nearest scene points. The entire object point cloud is translated by this average motion vector — the object's shape and orientation are preserved, but its centroid follows the local scene motion.

What this computes. This step propagates the object's 3D position from frame t to frame t+1 by tracking how the surrounding scene moved. If a cart moves forward, the scene points on the cart's surface have forward-pointing flow vectors, and the object placed on the cart inherits this forward motion. If the scene is static, the scene flow vectors are near zero (or reflect only camera motion, which is handled separately by the camera pose), and the object remains at its initial position.

Why averaging over $K$ nearest points. Averaging over $K$ points provides robustness to noise in individual flow estimates. A single flow vector might be erroneous due to textureless regions, occlusions, or estimation errors. By aggregating over a local neighborhood, the system obtains a stable estimate of the dominant motion direction around the object. This is an implicit assumption that the object moves rigidly with its immediate surroundings — that all nearby scene points share roughly the same 3D motion. This holds for objects resting on a single moving surface but would fail if the object were placed at the boundary between two independently moving surfaces (e.g., half on a table and half on a sliding door). The paper does not discuss this edge case.

Why not track the object directly. An alternative would be to track the object itself through the video using feature matching or optical flow directly on the rendered object. The paper's approach of tracking the surrounding scene instead is motivated by practicality: in the first frame, the object has not yet been inserted into the video, so there are no object pixels to track. By tracking the scene geometry the object is attached to, the system infers the object's motion indirectly without needing to detect the object in subsequent frames. This is particularly important because the video synthesis stage has not yet run — the object doesn't exist in the video until after the mask sequence is generated and the diffusion model runs. The mask generation stage operates entirely on the original (unmodified) source video plus the user's placement, so it cannot rely on seeing the inserted object in later frames.

The static fallback case. When the scene is static (no moving surfaces near the object placement), the scene flow vectors average to near zero, and the object remains at its initial 3D position. In this case, the object's apparent motion across frames is entirely due to camera movement, which is handled by the camera-aligned reprojection step. The scene flow propagation only introduces nonzero motion when local scene dynamics demand it. The ablation study (supplementary Figure 7) demonstrates the importance of this: without scene flow propagation, an object placed on a moving cart stays at its initial position while the cart moves out from under it, producing misaligned masks in later frames. With scene flow, the object moves with the cart.

Camera-Aligned Reprojection and Mask Extraction

Once the object's 3D point cloud $\{y'_{j,t}\}$ is determined for each frame (accounting for both the initial placement and scene flow propagation), the system must convert this 3D representation back into a 2D mask for each frame — telling the video synthesis model which pixels the object occupies.

Projection to the image plane. For each frame $t$ , each object point $y'_{j,t}$ (now in world coordinates, incorporating scene flow up to frame $t$ ) is projected onto the 2D image plane using the estimated camera parameters:

$\begin{bmatrix} u_{j,t} \\ v_{j,t} \\ 1 \end{bmatrix} \sim K \left( R_t \, y'_{j,t} + t_t \right)$

where:

$K$ is the 3×3 camera intrinsic matrix (focal length, principal point),
$R_t$ is the 3×3 rotation matrix for frame $t$ (camera orientation relative to world),
$t_t$ is the 3×1 translation vector for frame $t$ (camera position in world coordinates),
$(u_{j,t}, v_{j,t})$ are the resulting 2D pixel coordinates of point $j$ in frame $t$ .

The notation $\sim$ indicates equality up to a scale factor (homogeneous coordinates) — the left side is divided by the third coordinate to obtain pixel coordinates.

What this computes. For each frame, this operation takes every 3D point on the object's surface, applies the camera's rotation and translation to convert from world coordinates to camera coordinates, then applies the intrinsic matrix $K$ to convert from camera coordinates to pixel coordinates. The result is a set of 2D pixel locations indicating where each point on the object's surface falls on the image sensor. This is the standard pinhole camera projection model used throughout computer vision and graphics.

Why this form. The projection uses the full perspective camera model ( $K, R_t, t_t$ ) rather than an orthographic or weak-perspective approximation. This matters because real video footage, particularly with moving cameras, exhibits perspective effects: objects farther from the camera appear smaller, parallel lines converge, and the apparent shape of an object changes as the camera moves around it. An orthographic projection (which assumes all rays are parallel) would fail to capture these effects, producing objects that don't change scale with distance or that appear geometrically inconsistent with perspective cues in the scene. The use of estimated camera parameters (from the 4D reconstruction stage) rather than assuming a fixed or simplistic camera model enables the mask to accurately reflect viewpoint-dependent geometry.

Rasterization to produce the object silhouette. The projected 2D points $\{(u_{j,t}, v_{j,t})\}$ form a sparse set of pixel locations — they are individual points from the object's point cloud, not a dense mask. To obtain a dense binary mask, the system rasterizes these points: all pixels within the convex hull (or the interior) of the projected points are filled, producing a solid silhouette. The paper's description:

"By projecting and rasterizing all visible points, we obtain the object's silhouette for each frame, generating a synthetic object-inserted video sequence $\{\hat{I}_t\}_{t=1}^T$ ."

The intermediate result $\hat{I}_t$ is a synthetic rendering of the video with the object's geometry projected onto each frame — essentially a "placeholder" showing where the object would appear from the estimated camera viewpoints. This rendering accounts for camera motion (the projection changes as $R_t$ and $t_t$ change), parallax (objects closer to the camera displace more with viewpoint changes than distant objects), and occlusion (points on the far side of the object are not visible from the camera and are correctly hidden by the front-facing surfaces).

SAM2-based mask refinement. The projected silhouettes from the 3D point cloud are approximate — they capture the coarse geometry of the object as reconstructed by the single-view 3D model, which may not perfectly match the reference image. To obtain accurate, temporally coherent masks, the system processes the synthetic video $\{\hat{I}_t\}$ with SAM2 [20], a video segmentation model:

"To extract accurate object regions, the synthesized video $\{\hat{I}_t\}_{t=1}^T$ is processed by a SAM2 [20], producing a temporally aligned binary mask sequence $\{M_t\}_{t=1}^T$ , where each mask distinguishes object and background regions."

SAM2 (Segment Anything Model 2) is a promptable video segmentation model that can track objects across frames. Given the synthetic video showing the projected object in each frame, SAM2 identifies the object region and produces a clean, temporally coherent binary mask for each frame. The mask $M_t$ is a binary image where $M_t(u, v) = 1$ for pixels belonging to the inserted object and $M_t(u, v) = 0$ for background pixels.

Why use SAM2 rather than directly using the projected silhouettes. The projected silhouettes from the point cloud rasterization are subject to several sources of error:

The single-view 3D reconstruction [29] produces only an approximate shape — it cannot recover exact object contours, especially for thin structures or complex geometries.
The point cloud may have holes or sparse regions where depth estimation was uncertain.
Rasterization of a point cloud produces jagged boundaries that don't match the smooth contours of real objects.

SAM2 acts as a refinement step: it sees the approximate location and shape of the object in each frame (from the projection) and uses its learned video segmentation priors to produce clean, temporally consistent masks. Because SAM2 was trained on massive video segmentation datasets, it has strong priors about object boundaries and temporal consistency that the geometric projection lacks. The mask sequence $\{M_t\}$ is the final output of the mask generation stage and serves as the spatial conditioning input to the video synthesis stage.

What the mask sequence encodes. Critically, because the masks are generated from a 4D representation that includes both camera motion and scene dynamics, they faithfully encode occlusion relationships: if the object is placed behind a person who walks in front of it, the mask correctly disappears for the occluded frames (the object is not visible) and reappears when the occlusion ends. The mask is generated from the 3D point cloud, which has known depth — points behind the occluder are not visible and are not rasterized. This is the key capability that distinguishes the 4D-aware mask generation from simpler mask propagation methods: the mask is not simply a 2D region tracked through time; it is a geometrically derived visibility map that respects the 3D structure of the scene.

ROSE++ Dataset Construction

The ROSE++ dataset is the training data foundation that enables the video synthesis model to learn illumination-aware object insertion. It is constructed by extending the existing ROSE dataset [15] using a VLM-based object retrieval process.

The original ROSE dataset. ROSE (Remove Objects with Side Effects) [15] is a synthetic dataset designed for video object removal. Each sample in ROSE consists of three components:

An object-present video: a rendered 3D scene containing a target object along with its associated lighting effects (shadows cast by the object on the floor, reflections on nearby surfaces, illumination changes on adjacent objects).
The corresponding object mask video: per-frame binary masks indicating which pixels belong to the target object.
An object-removed video: the same scene rendered with the target object and all its optical side effects (shadows, reflections, illumination changes) completely eliminated.

The critical feature of ROSE is that the "removed" version is not simply a copy of the source with the object masked out — it is a physically re-rendered scene where the object was never present. This means the removed video shows the scene as it would naturally look without the object: shadows that the object cast are gone, surfaces that were darkened by the object's occlusion of a light source are brighter, reflections of the object in mirrors or shiny surfaces are absent. This makes ROSE uniquely suitable for training a model to understand that inserting an object requires generating lighting effects beyond the object's immediate boundary.

Inverting removal into insertion. The paper's key insight is that ROSE's removal triplets can be inverted to create insertion training pairs. The mapping is:

Source video = the object-removed video from ROSE (scene with no object and no side effects).
Target video = the object-present video from ROSE (scene with the object and all its lighting effects).
Mask video = the object mask video from ROSE (indicating where the object should be inserted).

This inversion means the model sees, during training, a source video that has no object and no shadows, and a target video that has both the object and its shadows. When the model learns to map source → target, it implicitly learns that inserting an object requires generating visible changes both inside the mask (the object itself) and outside the mask (shadows, reflections, illumination adjustments). This is supervised training for exactly the capability that self-supervised inpainting cannot provide, because inpainting training masks out a region and asks the model to fill it in — there is never a signal that regions outside the mask should change.

The missing component: reference object images. The original ROSE dataset does not include explicit reference object images because it was designed for removal, not insertion. For VOI, the model needs a reference image showing what the object looks like, which it uses as conditioning to determine the object's appearance (color, texture, shape) during generation. The paper must therefore generate reference images for each video in the dataset.

Naive approach: cropping from frames. The most straightforward approach would be to crop the object from a random frame of the object-present video (using the mask to isolate the object) and use that crop as the reference image. The paper argues this causes training-inference mismatch:

"Previous approaches directly cropped objects from random video frames to use as reference images, which caused the model to overfit to video-specific content and often led to copy-and-paste artifacts."

During training, if the reference image is a crop from the exact target video, the model can "cheat" by learning to copy the reference image directly into the masked region rather than learning a generalizable insertion capability. The reference image contains video-specific lighting, perspective, and background fragments that are not present during inference (where the reference image comes from a separate source — a user-provided product photo on a white background). At inference time, the model encounters a clean, white-background reference image and must generalize from the video-crop references it saw during training. The mismatch produces copy-paste artifacts: the model tries to find a direct pixel match between the reference and the target, which works when the reference is a crop but fails with clean reference images.

VLM-based object retrieval. To close this gap, the paper generates reference images using a Vision-Language Model (VLM) [1] — specifically GPT-4's image generation capability — conditioned on multi-view object crops from the video. The procedure is:

Sample frames containing the object: For each ROSE video, the system samples $n$ frames that contain the target object (from the object-present video). The paper does not specify the exact value of $n$ , but the supplementary material (Figure 3, Figure 8) describes the process.
Extract object regions: For each sampled frame, the object region is extracted using the provided mask — only the pixels inside the mask are kept, and the background is removed. This produces $n$ multi-view object crops showing the object from different viewpoints as it appears in the rendered video.
VLM generation: These multi-view object crops are provided to the VLM along with a detailed prompt (reproduced in Figure 8 of the supplementary material). The prompt instructs the VLM to:
- Regenerate the object cleanly on a pure white background.
- Keep only the object visible (remove all background fragments).
- Preserve the object's proportions and fine visual details.
- Ensure the object is fully visible (no cropping or truncation).
- Do not add shadows, reflections, or extra elements.
- Maintain consistency in geometry, color, and texture across viewpoints.
- Use multi-view reasoning to reconstruct missing or obscured areas.
The VLM produces $m$ candidate object images (the paper does not specify $m$ numerically).
DINO-based selection: The $m$ candidates are ranked by their visual similarity to the original object as it appears in the source video. For each candidate image $\hat{o}_k$ and each of the $N$ sampled frames $f_j$ (where $f_j$ is the object-present frame), a similarity score is computed:

$s_k = \frac{1}{N} \sum_{j=1}^{N} \text{DINO}(\hat{o}_k, f_j)$

where $\text{DINO}(\cdot, \cdot)$ is the cosine similarity between DINOv2 [16] feature embeddings of the candidate object image and the $j$ -th frame. The final reference image is selected as:

$o^* = \arg\max_{\hat{o}_k} s_k$

What this computes. For each candidate object image produced by the VLM, the system computes its average visual similarity to the $N$ frames of the video where the real object appears. The similarity metric uses DINOv2 features — deep visual features from a self-supervised vision transformer that capture semantic and structural similarity rather than low-level pixel matching. A high $s_k$ means the candidate image looks like the same object that appears in the video across multiple viewpoints. The candidate with the highest average similarity is selected as the reference image.

Why DINOv2. DINOv2 features are particularly suitable for this selection task because they are robust to variations in lighting, viewpoint, and background — exactly the differences between a clean white-background generated image and the in-video object renders. A pixel-space similarity metric (like mean squared error) would penalize the clean generated image for having a different background or slightly different lighting, even though those differences are actually desirable (the reference should not contain video-specific lighting). DINOv2's semantic features focus on object identity — shape, texture, color distribution — which is what matters for establishing that the reference image depicts the same object.

Why average over multiple frames. Averaging over $N$ frames ensures the similarity score reflects the object's appearance across multiple viewpoints, not just a single favorable angle. A candidate that looks similar from one viewpoint but different from another will have a lower average score and be less likely selected. This is crucial because the reference image must serve as conditioning for the object in all frames of the generated video, not just a single viewpoint.

Preventing copy-paste artifacts. The key advantage of VLM-generated reference images over frame crops is that the generated images are clean — white background, centered object, consistent lighting — matching the type of reference images users would provide at inference time (e.g., product photos). During training, the model sees these VLM-generated references paired with target videos, so it learns to map from a clean reference image to an in-scene object with appropriate lighting and shadows. At inference time, when the user provides a similarly clean reference image, the training-inference distribution matches, and the model generalizes correctly rather than attempting to find exact pixel copies.

The ablation study confirms this quantitatively (Table 2, supplementary): models trained with random-frame object crops achieve Multi-View Consistency of 0.5295, while models trained with VLM-generated references achieve 0.5857 — a substantial improvement. The "random" variant (denoted Ours_random in Table 2) exhibits copy-paste behavior that produces inconsistent object appearances across different viewpoints, because during training it learned to replicate video-specific crops rather than to generalize from clean reference images.

Dataset scale and composition. The paper does not explicitly state the number of videos in ROSE or ROSE++. ROSE [15] is described as a "synthetic collection," and the supplementary material indicates training on a "single NVIDIA H200 GPU" for "approximately 40 hours" with 5,000 iterations, suggesting a moderately-sized dataset (likely hundreds to low thousands of videos, given the per-iteration batch processing). The exact size is not critical for understanding the method, but it indicates the scale: this is not a web-scale dataset but a carefully constructed synthetic one.

Video Object Insertion Model (First-Frame Inpainting and Video Diffusion)

With the mask sequence $\{M_t\}$ and the reference object image in hand, the second stage generates the final video with the object inserted and photometrically integrated into the scene. This stage has three key design elements: first-frame inpainting for anchor quality, LoRA fine-tuning on ROSE++ for illumination awareness, and the full video diffusion pipeline.

Base model: Wan2.1-VACE-14B. The paper uses Wan2.1-VACE-14B [9] as the pretrained video generation backbone. VACE (All-in-one Video Creation and Editing) is a diffusion-based model designed for video editing tasks, including inpainting, outpainting, and reference-guided generation. It operates at a spatial resolution of 832 × 480 pixels with 81 frames per clip (the supplementary material specifies these as the training and generation resolution). The model uses a VAE (Variational Autoencoder) for latent-space encoding and decoding, and transformer blocks for the denoising process.

VACE is chosen over other video diffusion models because it was designed specifically for video editing — it accepts conditioning inputs such as masks and reference images, making it directly applicable to the VOI task without architectural modification. The model was pretrained on large-scale video data, giving it strong priors about object appearance, motion, and temporal coherence.

Fine-tuning strategy: LoRA on ROSE++. Rather than full fine-tuning (which would require updating all 14 billion parameters and risk catastrophic forgetting of the pretrained video generation capabilities), the paper uses Low-Rank Adaptation (LoRA) [6] for efficient fine-tuning. LoRA injects trainable low-rank matrices into the attention layers of the transformer blocks, while keeping the original pretrained weights frozen. The number of trainable parameters is orders of magnitude smaller than the full model, enabling training on a single GPU.

Training hyperparameters (from supplementary material):

Optimizer: Presumably AdamW (standard for diffusion model fine-tuning, though not explicitly stated).
Learning rate: $1 \times 10^{-4}$ (relatively high for LoRA, reflecting the small number of trainable parameters).
LoRA rank: 128 (controls the dimensionality of the low-rank adaptation; higher rank = more capacity for learning task-specific patterns at the cost of more parameters. Rank 128 is on the higher end of typical LoRA configurations, suggesting the insertion task requires significant adaptation from the pretrained weights).
Training iterations: 5,000 (the model sees 5,000 batches of ROSE++ training data).
GPU: Single NVIDIA H200 with 141GB of memory (the training fits on one GPU despite the 14B parameter base model because only the LoRA parameters are optimized).
Training time: Approximately 40 hours.

Inference configuration:

Denoising steps: 50 (the number of diffusion sampling steps; 50 is a moderate choice balancing quality and speed — fewer steps are faster but produce noisier results; more steps approach the quality ceiling but with diminishing returns).
Resolution: 832 × 480 with 81 frames per clip (matching the training resolution; videos longer than 81 frames would presumably be processed in overlapping clips or with temporal tiling, though the paper does not specify the handling of longer sequences).

First-frame inpainting strategy. A key architectural choice is that the first frame is not generated by the video diffusion model directly. Instead, the paper uses a pretrained image inpainting model [31] (OmniPaint) to generate the first frame with the object inserted at the mask location:

"Since current image inpainting models [31] demonstrate stronger visual reconstruction capability than video models, the first frame is generated with high fidelity to serve as a reliable visual reference."

OmniPaint [31] is a state-of-the-art image inpainting model designed for object-oriented editing via disentangled insertion-removal. It takes the first frame of the source video, the reference object image, and the first frame's mask $M_1$ , and produces an inpainted frame where the object appears in the masked region with high visual fidelity.

Why image inpainting for the first frame. Image inpainting models have several advantages over video models for single-frame quality:

They can be trained on much larger and more diverse image datasets (billions of images) compared to video datasets (millions of clips at best).
They don't need to model temporal consistency, allowing them to allocate all capacity to per-frame visual quality.
The state of image inpainting, particularly for object insertion with reference images, is more mature than the video counterpart — models like OmniPaint have been specifically optimized for reference-guided object insertion.

The tradeoff is that the image model has no temporal context — it generates the first frame in isolation, without knowing what happens in later frames. This is acceptable because the first frame's role is to establish a high-quality visual anchor: it determines the exact appearance, color, texture, and lighting of the inserted object in the initial viewpoint. The video model then propagates this appearance through time, maintaining consistency.

How the first frame conditions the video model. The paper uses a first-frame-guided video generation strategy similar to [5] (Keyframe-guided creative video inpainting). The first frame (with the object inserted by the image inpainting model) is provided as an additional conditioning input to the video diffusion model alongside the source video, mask sequence, and reference object image. Specifically (Section 3.4):

"the first frame is generated with high fidelity to serve as a reliable visual reference. The visual information from this frame is then effectively propagated throughout the subsequent video generation process, maintaining consistent color, texture, and lighting of the inserted object."

The video model sees the first frame as a "ground truth" example of what the object should look like at $t=1$ . Its job for frames $t=2, 3, ..., T$ is to generate the object with the same visual properties (color, material, shape) but with appropriate viewpoint-dependent changes (the object should shift perspective as the camera moves, reflect different lighting as the scene lighting changes, become partially occluded as foreground elements pass in front of it).

The exact conditioning mechanism is not described in architectural detail, but it follows the VACE model's design for reference-guided generation: the first frame is encoded through the VAE and provided as a spatial conditioning signal, analogous to how ControlNet or similar adapter-based conditioning works for diffusion models. The video model's attention mechanism can attend to the first frame's features when generating each subsequent frame, enabling it to copy appearance properties while adapting geometry.

What the ablation reveals about this component. The ablation study (Figure 5, Table 3 in supplementary) quantifies the contribution of first-frame inpainting. Without it (configuration (b), which uses the 4D-aware mask but generates all frames including the first with the video model), subject consistency metrics are lower: CLIP-I is 0.7532 vs. 0.7880 with first-frame inpainting (configuration (c)), and DINO-I is 0.3861 vs. 0.5135. The substantial jump in DINO-I (from 0.39 to 0.51) is particularly notable because DINO features are more sensitive to structural and semantic object identity than CLIP features — the image inpainting model produces a first-frame object that is structurally more faithful to the reference, and the video model propagates this structural fidelity through the sequence.

How ROSE++ training enables illumination-aware synthesis. The central challenge the video synthesis model must address is generating lighting effects beyond the mask boundary. Standard inpainting training (mask-out-and-reconstruct) cannot teach this because the loss is only computed inside the masked region. ROSE++ changes the training paradigm:

Training input: The source video (object-removed ROSE video, where the object and its shadows are absent) + the mask sequence (indicating where the object should go) + the VLM-generated reference object image + the first frame with the object inserted (generated by applying the image inpainting model to the first frame, following the same procedure as inference).
Training target: The target video (object-present ROSE video, where the object and its shadows/reflections/illumination effects are present everywhere in the frame).
Loss: The diffusion model's denoising loss is computed over the entire frame, not just the masked region. This means the model is penalized for failing to generate the shadow on the floor (which is outside the mask) or failing to brighten a wall where the object occludes a light source.

During training, when the model generates a candidate frame, the loss compares it to the target frame everywhere — inside the mask (where the object should appear), in the immediate vicinity (where shadows and reflections should appear), and in the distant background (which should remain unchanged, as the object's lighting effects don't extend infinitely). The model must learn to:

Generate the object with the correct appearance inside the mask (subject fidelity).
Generate appropriate shadows, reflections, and illumination changes in regions adjacent to the mask (photometric consistency).
Preserve all other content exactly as in the source video (background preservation).

This is a substantially harder learning problem than mask-constrained inpainting, but it is the correct training objective for realistic insertion. The model learns the physical relationship between objects and their optical effects — that a solid object placed between a light source and a surface casts a shadow extending away from the light, that shiny objects produce specular highlights on adjacent surfaces, that an object occluding ambient light darkens the region behind it.

The LoRA module's role. The LoRA adaptation on ROSE++ specifically tunes the model to handle this illumination synthesis task. The pretrained VACE model already knows how to generate coherent video and how to inpaint within masks (from its original training). The LoRA fine-tuning adds the capability to generate lighting effects beyond the mask while preserving background content. The low-rank adaptation is applied to the attention layers, which are responsible for determining which parts of the conditioning (source video, reference image, mask, first frame) to attend to when generating each output pixel. By tuning these attention patterns, the model learns to look at the reference object, infer its material properties and approximate 3D shape, look at the scene lighting (inferred from the source video's illumination patterns), and generate appropriate shadows and reflections at the correct locations.

Ablation evidence for ROSE++ effectiveness. The ablation study (Figure 6, qualitative; Table 3, quantitative) demonstrates this capability. Figure 6(a) shows a door opening and closing sequence: without LoRA fine-tuning on ROSE++, the inserted paper bag maintains constant brightness regardless of door state (open door = sunlight floods in, closed door = darkness). After fine-tuning, "the illumination on the object dynamically responds to the scene lighting, the bag appears brighter when sunlight enters through the open door and darker when the door is closed." Figure 6(b) shows shadow generation: without ROSE++ fine-tuning, the model "fails to infer that shadows should naturally extend beyond the object mask, resulting in behavior similar to a simple inpainting model." After fine-tuning, "the model learns to infer the global light direction and intensity, allowing realistic shadows to be synthesized on surrounding surfaces outside the mask."

Quantitatively, adding ROSE++ LoRA fine-tuning (comparing Ours to configuration (c) in Table 3) improves VBench Background Consistency from 0.9175 to 0.9429, Subject Consistency from 0.9290 to 0.9520, and Imaging Quality from 0.6318 to 0.7101. The Background Consistency improvement is particularly telling — it measures whether regions outside the inserted object remain faithful to the source video. The improvement indicates that the model with ROSE++ fine-tuning does a better job of preserving background content while simultaneously generating lighting effects — it knows where to change the scene and where to leave it untouched. Without ROSE++, the model either overwrites too much (reducing Background Consistency) or generates no lighting effects at all (producing unrealistic insertions).

The full inference pipeline. At inference time, given a new source video and reference object:

Mask generation: The 4D-aware mask generation module produces $\{M_t\}$ .
First frame inpainting: OmniPaint generates the first frame with the object inserted at $M_1$ , using the reference image as conditioning.
Video generation: The fine-tuned VACE model processes the full sequence:
- Conditioning inputs: Source video (all 81 frames), mask sequence $\{M_t\}$ , reference object image, first inpainted frame.
- Diffusion process: Starting from random noise in the VAE latent space, the model iteratively denoises for 50 steps. At each step, the transformer blocks attend to the conditioning inputs, guided by the LoRA-adapted attention patterns that know to generate shadows and lighting effects where appropriate.
- Output: The VAE decoder converts the denoised latents back to pixel space, producing the final 81-frame video at 832 × 480 resolution.

The paper does not specify the handling of videos longer than 81 frames (would they be processed in overlapping clips with temporal blending?), which is a practical detail left to implementation.

Summary of Design Choices and Their Justifications

Two-stage decomposition (masks then synthesis): Separates the geometric problem (where the object goes, solved by 4D reconstruction) from the appearance problem (what it looks like, solved by diffusion). This allows each stage to use the most appropriate tool and avoids requiring the diffusion model to implicitly learn 3D geometry from 2D training data.
Uni4D for 4D reconstruction: Leverages pretrained foundation models (depth, flow, segmentation) rather than training a dedicated 4D reconstruction network from scarce multi-view video data. The tradeoff is reconstruction quality bounded by component model accuracy.
Scene flow propagation via $K$ -nearest-point averaging: Provides robust object motion tracking by aggregating local flow estimates, handling dynamic scenes where the object's supporting surface moves. Avoids needing to track the (not-yet-inserted) object directly.
SAM2 for mask refinement: Converts approximate point cloud projections into clean, temporally coherent masks using learned video segmentation priors, compensating for geometric reconstruction inaccuracies.
Inverting ROSE into ROSE++: Provides supervised training for illumination-aware insertion without requiring expensive collection of real insertion pairs. The inversion guarantees that the training target includes all optical side effects of the object.
VLM-generated reference images over frame crops: Prevents training-inference distribution mismatch and the resulting copy-paste artifacts. Ensures the model generalizes to clean reference images at inference time.
First-frame image inpainting + video propagation: Exploits the superior per-frame quality of image models for the anchor frame, then propagates established appearance through time with the video model. Compensates for video models' relatively lower single-frame quality.
LoRA fine-tuning (rank 128) rather than full fine-tuning: Enables training on a single GPU with 14B-parameter model, preserves pretrained video generation capabilities, and specifically adapts attention patterns for the insertion task.
Loss computed over the entire frame during ROSE++ training: The critical difference from inpainting training — the model is explicitly supervised to generate lighting effects outside the mask while preserving distant background. This is what enables shadow and illumination synthesis.

4. Key Insights and Innovations

Innovation 1: Decomposing Video Object Insertion into Orthogonal Geometric and Photometric Subproblems

The dominant assumption in prior video object insertion work — both research and commercial — is that VOI is fundamentally a video generation problem that can be solved by a single end-to-end model. Whether through inpainting within masks (INVI [21], VideoAnyDoor [25]), propagating edited first frames (GenProp [14]), or text-guided full-frame generation (Pika Pro, Kling), the prevailing paradigm treats VOI as an appearance synthesis task where geometry is either implicitly learned from data, provided as dense per-frame masks, or bypassed entirely via language instructions.

InsertAnywhere makes a fundamentally different conceptual move: it asserts that VOI is two separate problems that require two different types of reasoning, and that conflating them in a single model is precisely why prior approaches fail in complementary ways. The geometric problem — determining where an object should appear in 3D space across frames given camera motion and occlusions — requires explicit spatial reasoning that diffusion models are architecturally poorly suited for. The photometric problem — generating the object's appearance and its lighting effects — requires the learned visual priors and generative capability that diffusion models excel at. The paper's architecture directly embodies this decomposition: the mask generation module solves the geometric problem using 4D reconstruction, and the video synthesis module solves the photometric problem using a diffusion model conditioned on the geometry-derived masks.

This is not merely an engineering convenience. It is a diagnostic reframing of the VOI problem that explains why prior methods exhibit the specific failure patterns they do. Inpainting methods fail on lighting because they lack photometric supervision beyond mask boundaries — a photometric deficiency. Propagation methods fail on occlusions because they lack 3D understanding of visibility — a geometric deficiency. Commercial tools fail on object placement and fidelity because text is an inadequate interface for specifying precise 3D coordinates — both a geometric deficiency (wrong placement) and a photometric one (wrong appearance due to missing spatial conditioning). By decomposing the problem, the paper can attribute each failure mode to a specific missing capability and design targeted solutions rather than hoping a larger model trained on more data will somehow learn everything implicitly.

The empirical validation of this decomposition is in the ablation study (Figure 5, Table 3). Configuration (a), which uses a naive mask without 4D understanding, fails on occlusions — a geometric failure. Configuration (b), which adds 4D-aware masks, fixes occlusions but produces objects with poor fidelity because the video model alone cannot match the image inpainting model's per-frame quality — a photometric deficiency. Configuration (c), which adds first-frame inpainting, fixes fidelity but still cannot generate shadows because the model was never trained with insertion supervision — another photometric deficiency. Only the full system, which addresses both geometric and photometric requirements with specialized modules, produces videos that are simultaneously geometrically accurate and photometrically realistic. This sequential ablation directly validates the decomposition: each component fixes a distinct, non-overlapping failure mode, confirming that the two problem aspects are genuinely orthogonal.

Significance beyond performance: This decomposition is a conceptual contribution that generalizes beyond this specific system. It suggests that other video editing tasks that require precise spatial reasoning combined with realistic appearance synthesis — virtual object removal with shadow elimination, scene element rearrangement, multi-object compositing — might similarly benefit from explicit 3D scene understanding rather than relying solely on end-to-end generative models. It also provides a framework for diagnosing failures in future VOI systems: if an object is geometrically correct but looks like a cutout, the photometric module needs improvement; if it looks photorealistic but floats or penetrates geometry, the geometric module is failing. This diagnostic clarity is absent from end-to-end approaches where failures are opaque.

Fundamental vs. incremental: This is a fundamental conceptual reframing, not an incremental improvement. The paper is not proposing a better inpainting architecture or a better diffusion sampling schedule; it is redefining what the VOI problem is at the architectural level. The two-stage design is the direct expression of this reframing.

Innovation 2: Inverting Object Removal into Object Insertion Training via Synthetic Data

The field's central bottleneck for VOI has been the absence of supervised training data. To train a model to insert an object into a video, you need video triplets: a source video without the object, a target video with the object naturally integrated (including lighting effects), and a reference image of the object. Collecting such triplets from real footage is essentially impossible — you would need to film every scene twice, once with and once without the object, under identical lighting and camera motion. Prior work circumvented this by training with self-supervised inpainting objectives (mask out a region, reconstruct it) or by relying on pretrained generative priors without insertion-specific fine-tuning. Both approaches share a critical limitation: the model never sees examples where an inserted object causes photometric changes outside its immediate mask region, so it never learns to generate shadows, reflections, or global illumination adjustments.

The ROSE++ dataset construction represents a genuinely innovative solution to this data problem. The key insight is that object removal and object insertion are inverse tasks, and a dataset designed for one can be inverted to serve the other — provided the removal dataset was constructed with photometric fidelity (removing shadows and reflections, not just the object itself). The ROSE dataset [15] was explicitly built for "removing objects with side effects," meaning its object-removed videos are physical re-renderings where the object and all its optical consequences are eliminated. By swapping which video serves as input and which as target, the paper transforms removal training pairs into insertion training pairs where the training signal spans the entire frame, not just the masked region.

This inversion is clever but would be insufficient on its own — ROSE lacks reference object images because removal doesn't require them. The paper's second insight is to use a VLM to generate clean, white-background reference images from multi-view object crops extracted from the ROSE videos. This solves two problems simultaneously: it completes the missing component of the training triplet, and it ensures the reference images match the distribution users will provide at inference time (clean product photos or generated object images), preventing the training-inference mismatch that causes copy-paste artifacts when models are trained on frame crops.

The quantitative evidence (Table 2) confirms that this matters: models trained with random-frame object crops achieve Multi-View Consistency of 0.5295, while VLM-generated references achieve 0.5857 — a ~10.6% relative improvement on the metric most directly measuring whether the model copies video-specific pixels rather than generalizing object identity. The qualitative evidence (Figure 6) shows that ROSE++ fine-tuning is what enables shadow generation and illumination adaptation — capabilities entirely absent from the non-fine-tuned baseline, which behaves "similar to a simple inpainting model."

Significance beyond performance: This data construction strategy — inverting a task with ground-truth paired data and using VLMs to fill missing components — is a methodological template that extends beyond VOI. Any video editing task where paired before/after data exists for one direction could potentially be inverted to train the reverse direction, with VLMs or other generative models synthesizing any missing conditioning signals. For example, a dataset for adding fog to clear videos could be inverted to train fog removal, or a dataset for aging faces could be inverted to train de-aging. The key requirement is that the original dataset captures the full photometric consequences of the edit, not just the local region of change — which is precisely what made ROSE suitable for inversion while standard inpainting datasets are not.

Fundamental vs. incremental: This is a fundamental contribution to the data methodology for video editing, not merely an incremental dataset paper. The paper is not just releasing another dataset; it is demonstrating a generalizable strategy for constructing supervised training data for tasks that lack naturally occurring paired examples. The combination of task inversion and VLM-based component generation is a pattern that other video editing tasks can adopt.

Innovation 3: Explicit 4D Scene Reconstruction as an Interface Between User Intent and Generative Models

The interface between user intent and generative video models is an under-examined problem in the video editing literature. The dominant paradigm in commercial tools (Pika Pro, Kling) is text-based: the user describes what they want in natural language, and the model interprets this description to produce the edited video. Research methods often use dense per-frame masks, but these are impractical for users to provide. InsertAnywhere proposes a third paradigm: the user specifies object placement once, in 3D, and the system automatically propagates that intent through time and viewpoint using explicit 4D scene understanding.

The conceptual innovation here is that 4D reconstruction serves as an intent propagation mechanism, not just a geometric computation. The user's placement in the first frame — the position, rotation, and scale of the object — encodes their intent about the object's spatial relationship to the scene. The 4D reconstruction translates this single-frame intent into a full-video specification by computing how that spatial relationship evolves given camera motion and scene dynamics. The mask sequence that emerges is the geometric consequence of the user's intent, not an independent specification that the user must provide.

This is fundamentally different from how prior work handles the user-to-model interface. GenProp [14] also starts from a user-edited first frame, but it propagates 2D appearance changes rather than 3D geometric intent — it doesn't understand that the object is a 3D entity with a persistent spatial location, so it cannot handle occlusions where the 2D appearance temporarily disappears. Commercial tools use language, which the paper's experiments show is systematically inadequate for spatial precision — objects appear at wrong scales, in wrong locations, and with wrong identities (Figures 4, 9, 10). Per-frame mask methods put the burden of temporal consistency on the user, which is impractical.

The paper's approach — one-shot 3D placement propagating through 4D reconstruction — sits in a sweet spot that no prior work occupies. It provides the spatial precision that language interfaces lack (the user places a 3D point cloud at an exact world-coordinate location) while avoiding the per-frame annotation burden that mask-based methods require. The GUI described in Section 3.2.2 and shown in Figure 2 is not just a convenience feature; it is the embodiment of this interaction paradigm — the user navigates a 3D representation of the scene, places the object relative to visible geometric features (table surfaces, floor planes), and the system handles everything temporal.

Why this is non-obvious: The computer vision field has largely treated 4D reconstruction from monocular video as a standalone research problem — can we recover accurate geometry and camera motion? The paper repurposes it as an HCI (human-computer interaction) mechanism for conveying spatial intent to generative models. The reconstruction doesn't need to be perfect to be useful; it only needs to be accurate enough that the propagated masks produce visually plausible object trajectories. The generative model's visual priors can compensate for reconstruction inaccuracies during synthesis. This shifts the evaluation criterion for 4D reconstruction in this context from geometric accuracy (how close are the estimated depths to ground truth?) to downstream task utility (do the propagated masks enable realistic insertion?). The paper's positive results (occlusion handling in Figure 4, cart-tracking in supplementary Figure 7) validate that existing reconstruction quality is already sufficient for this purpose, even though the component models (depth estimation, optical flow) are themselves imperfect.

Significance beyond performance: This interaction paradigm suggests a broader role for explicit 3D scene understanding in creative tools. Rather than asking users to describe spatial relationships in language or to manually annotate every frame, future video editing systems could allow users to place objects, lights, or effects in a reconstructed 3D space and have the system automatically handle temporal consistency and viewpoint changes. This bridges the gap between the precision of 3D content creation tools (Blender, Unreal Engine) and the accessibility of AI-based video editing — the user gets 3D control without needing 3D modeling expertise.

Fundamental vs. incremental: This is a fundamental shift in the user interaction paradigm for video object insertion. It is not an improvement to an existing interface but a new category of interface — one-shot spatial placement mediated by 4D scene understanding — that no prior VOI system offers. The evidence that this paradigm works where text and per-frame masks fail is the paper's core qualitative result: occlusion handling that commercial tools cannot replicate (Figure 4, pepper shaker example) and dynamic object tracking that naive propagation cannot achieve (Figure 7, cart example).

Innovation 4: Diagnosing Verifier vs. Proposer Failures in Video Generation Through Ablation Structure

While not framed in these terms by the paper, the ablation study (Figure 5, Table 3) implicitly introduces a diagnostic framework for video editing systems that parallels the proposer-verifier decomposition familiar from language model test-time compute research. By sequentially adding components and measuring distinct failure modes, the paper demonstrates that VOI quality degrades in categorically different ways depending on which capability is missing — and that these failure modes do not overlap.

Configuration (a) — naive mask without 4D understanding — produces failures characterized by incorrect geometric placement under occlusion: "the model fails to maintain the original video when occlusion occurs... information such as the position of the person's arm and scarf [is lost] compared to the source video." This is a mask quality failure: the spatial conditioning signal given to the generator is wrong, so the generator produces geometrically incorrect output despite having the capability to generate realistic pixels.

Configuration (b) — 4D-aware mask without first-frame inpainting — fixes occlusion handling but produces failures characterized by low object fidelity: "the object's fidelity remains low." This is a generation quality failure: the spatial conditioning is correct, but the generator's per-frame synthesis capability is insufficient to faithfully reproduce the reference object's appearance. The mask is right, but the pixels inside it look wrong.

Configuration (c) — adding first-frame inpainting — fixes object fidelity but introduces failures characterized by missing photometric effects and temporal inconsistency: "temporal inconsistencies still arise after occlusion events, manifesting as fluctuations in attributes like scarf direction or surface texture." This is a training data / model capability failure: the generator can produce high-fidelity single frames but hasn't been trained to model the physical consequences of object insertion (shadows, illumination changes) or to maintain temporal coherence of these effects.

The full system — adding ROSE++ fine-tuning — fixes photometric effects, producing "geometrically accurate videos with high fidelity to the object across the entire sequence."

What makes this diagnostic framework valuable. This ablation reveals that the different failure modes are orthogonal and non-compensatory. Improving mask quality doesn't fix low object fidelity; improving per-frame generation quality doesn't fix missing shadows. Each component addresses a failure that the other components cannot. This is strong evidence that the paper's decomposition of VOI into geometric and photometric subproblems is not merely an architectural convenience but reflects a genuine structure in the task — these are independent requirements that must be independently satisfied.

The diagnostic value extends beyond this paper. For any future VOI system, this ablation structure provides a template for identifying which component is failing: if the inserted object floats or penetrates scene geometry, the mask/spatial conditioning module is at fault. If the object looks like a blurry copy of the reference, the generation module's fidelity is insufficient. If the object looks photorealistic but casts no shadow, the training data or model architecture lacks photometric supervision. This is significantly more actionable than an end-to-end system where a quality drop could stem from any combination of causes.

Comparison to prior work's evaluation practices. Prior VOI papers typically report aggregate metrics (CLIP-I, FID, user studies) without decomposing performance by failure type. The ablation in this paper effectively performs a controlled capability ablation — removing one capability at a time and measuring the specific degradation it causes — which provides causal evidence for each component's contribution rather than merely correlational evidence from aggregate metrics. The quantitative results in Table 3 support this: each component addition produces improvements on the specific metrics that measure its targeted failure mode (e.g., first-frame inpainting improves DINO-I from 0.3861 to 0.5135, a metric directly measuring object-structural fidelity, while ROSE++ fine-tuning improves Background Consistency from 0.9175 to 0.9429, measuring the model's ability to preserve non-object regions while generating lighting effects).

Significance beyond performance: This is a methodological contribution — a template for how to evaluate modular video editing systems through sequential capability ablation with failure-mode-specific metrics. It is not claimed as a theoretical advance by the paper, but it represents a more rigorous evaluation paradigm than the field's typical end-to-end metric comparison, and it directly validates the paper's central claim that geometric and photometric requirements are orthogonal.

Fundamental vs. incremental: This is incremental as a formal methodology (the paper does not propose a new evaluation framework or explicitly name it as a contribution) but conceptually significant as embedded practice — the ablation structure implicitly teaches the reader how to think about VOI system failures in a way that prior work's evaluations do not.

5. Experimental Analysis

Evaluation Methodology

Dataset. The paper introduces VOIBench, a new benchmark of 50 video clips spanning indoor scenes, outdoor environments, and natural landscapes. Each video is evaluated with two different objects (contextually relevant to the scene), yielding 100 total evaluation samples. The clips are drawn from diverse real-world settings, but the paper does not disclose the source of the original footage or whether it was collected by the authors or sourced from existing datasets.
Base model(s). The video generation backbone is Wan2.1-VACE-14B [9], a 14-billion-parameter diffusion-based video creation and editing model. It is fine-tuned with LoRA (rank 128) on the ROSE++ dataset for 5,000 iterations at a learning rate of 1×10⁻⁴ on a single NVIDIA H200 GPU (~40 hours). For first-frame inpainting, the paper uses OmniPaint [31], a pretrained image inpainting model. The mask generation stage builds on Uni4D [30] for 4D reconstruction, leveraging pretrained depth [17], optical flow [10], and segmentation [4, 11, 13] models without additional training.
Metrics. Three categories are evaluated:
- Subject consistency using CLIP-I [19] and DINO-I [16]: cosine similarity between CLIP or DINOv2 embeddings of the inserted object in generated frames and the reference object image. These are computed on subject-masked regions only, across 10 evenly spaced frames per video.
- Video quality using VBench metrics [8]: Imaging Quality (technical quality), Background Consistency (whether non-object regions remain faithful to the source video), Subject Consistency (whether the inserted object maintains its identity across frames), and Motion Smoothness (temporal coherence of motion).
- Multi-View Consistency using a VBench metric [8] evaluating how reliably the inserted object is maintained across viewpoints, particularly when occlusions occur with other scene objects.
Baselines. Two commercial state-of-the-art methods serve as baselines: Pika Pro [18] (via its "Pika Additions" feature) and Kling [23] (Kling AI). Both are closed-source, publicly accessible video generation tools with object insertion capabilities. The paper does not compare against any open-source research methods (e.g., INVI [21], VideoAnyDoor [25], GenProp [14]) in the quantitative evaluation.
Generation budget / compute accounting. No explicit compute budget matching is performed between InsertAnywhere and the baselines. The baselines are commercial tools accessed through their public APIs, making FLOPs or runtime comparisons impractical. Within InsertAnywhere's ablation, all configurations use the same base model (Wan2.1-VACE-14B) and inference settings (50 denoising steps, 832×480 resolution, 81 frames), so compute is implicitly controlled. The mask generation stage's computational cost (4D reconstruction, scene flow computation, SAM2 processing) is not quantified or compared to the video generation cost.
Cross-validation / statistical protocol. No cross-validation or statistical significance testing is reported. The evaluation uses the full VOIBench (100 samples) without describing train/validation/test splits. The user study (20 participants, 10% of test videos sampled randomly, 6 evaluation questions per video) provides some statistical signal through preference percentages, but no confidence intervals or significance tests are reported for the user study or the automated metrics.

Main Quantitative Results

Comparison Against Commercial Baselines

Table 1 presents the head-to-head comparison between InsertAnywhere, Pika-Pro, and Kling across all metrics on VOIBench (100 evaluation videos).

Subject consistency results. InsertAnywhere achieves CLIP-I of 0.8122 and DINO-I of 0.5678, substantially outperforming both baselines. Kling achieves 0.6349 CLIP-I and 0.5028 DINO-I, while Pika-Pro achieves 0.4940 CLIP-I and 0.3856 DINO-I. The gaps are significant on both metrics: InsertAnywhere outperforms Kling by +0.1773 on CLIP-I (+27.9% relative) and +0.0650 on DINO-I (+12.9% relative). The DINO-I metric is particularly informative for object identity because DINOv2 features capture structural and semantic object properties more faithfully than CLIP features, which can be influenced by lighting and background.

The interpretation: Kling produces objects that are recognizably related to the reference but often differ in fine structural details (e.g., the drawer with four compartments instead of three in Figure 4; object swapping in Figure 9). Pika-Pro's low DINO-I (0.3856) reflects severe identity drift — the generated objects bear limited structural resemblance to the reference. InsertAnywhere's high scores are attributed to the first-frame inpainting strategy, which uses a dedicated image model to faithfully reproduce the reference object's appearance in the anchor frame, after which the video model propagates this high-fidelity appearance through time.

Video quality results. InsertAnywhere achieves the highest scores across most VBench metrics (Table 1):

Imaging Quality: 0.7101 (Ours) vs. 0.7069 (Kling) vs. 0.6546 (Pika-Pro). The margin over Kling is very small (+0.0032), suggesting that all methods produce technically competent frames at comparable resolutions. Pika-Pro trails noticeably.
Subject Consistency: 0.9520 (Ours) vs. 0.9494 (Kling) vs. 0.8720 (Pika-Pro). The margin over Kling is again small (+0.0026). This metric measures temporal stability of the object's appearance within the generated video (distinct from the CLIP-I/DINO-I metrics, which compare against the reference image). Kling and InsertAnywhere both maintain good within-video consistency once the object is generated, even though Kling's object may not match the reference as faithfully.
Background Consistency: 0.9429 (Ours) vs. 0.9335 (Kling) vs. 0.9080 (Pika-Pro). InsertAnywhere has a clearer advantage here (+0.0094 over Kling). The paper attributes this to the precise mask-based spatial conditioning, which tells the model exactly which regions to modify and which to preserve. Commercial tools, lacking explicit masks, sometimes alter background regions unintentionally — the paper's qualitative analysis notes that "Kling and Pika-Pro often introduce undesirable changes to the background of the original video" (Section 4.3).
Motion Smoothness: 0.9916 (Ours) vs. 0.9940 (Kling) vs. 0.9889 (Pika-Pro). All three methods score very high and nearly identically on this metric, suggesting that temporal coherence of motion is not a key differentiator. This is consistent with the fact that all three methods use video-capable generative models with strong temporal priors.

Multi-View Consistency results. InsertAnywhere achieves 0.5857, compared to 0.5439 for Kling and 0.5123 for Pika-Pro (Table 1). The margin over Kling is +0.0418 (+7.7% relative). This metric specifically evaluates how well the inserted object handles occlusions and viewpoint changes — exactly the capability the paper claims the 4D-aware mask module provides. The score breakdown confirms that Kling struggles with occlusion scenarios (as illustrated in Figure 4, pepper shaker example, where the object appears in front of the hand instead of moving behind it). Pika-Pro scores lowest, consistent with its inability to handle scene geometry constraints.

User study results. The supplementary material (Table 4) reports user preferences from 20 participants evaluating 10% of test videos across six criteria:

Object Realism: 79.09% (Ours) vs. 19.09% (Kling) vs. 1.82% (Pika-Pro)
Lighting Consistency: 71.82% vs. 25.45% vs. 3.64%
Occlusion Integrity: 86.67% vs. 13.33% vs. 0.00%
Object-Video Consistency: 76.36% vs. 21.82% vs. 1.82%
Background Preservation: 80.91% vs. 16.36% vs. 2.73%
Overall Naturalness: 70.00% vs. 24.55% vs. 4.55%

Several patterns emerge from the user study. First, InsertAnywhere's strongest advantage is on Occlusion Integrity (86.67% preference vs. 13.33% for Kling and 0% for Pika-Pro) — users can clearly identify when an object fails to respect scene geometry during occlusion events, and the commercial tools perform essentially at chance or worse on this dimension. Second, Pika-Pro receives near-zero user preference across all criteria, consistent with its low automated metric scores. Third, Kling maintains non-trivial user preference on Lighting Consistency (25.45%) and Overall Naturalness (24.55%), suggesting that while its geometric placement is inferior, its photometric quality is sometimes competitive — consistent with its higher but still trailing automated metrics.

The paper notes that "the order of the three results shown in each candidate was randomly shuffled for every participant" to avoid positional bias. However, this is a small user study (20 participants, only 10% of the 100-sample test set ≈ 10 videos), and no statistical significance tests are reported, so the preference percentages should be interpreted as indicative rather than statistically robust effect sizes.

Ablation Studies and Robustness Checks

The paper conducts a structured ablation study (Figure 5, qualitative; Table 3 in supplementary, quantitative) by sequentially adding components to isolate their individual contributions. Each configuration uses the same base video generation model (Wan2.1-VACE-14B) and inference settings.

Configuration (a) — Naive mask without 4D understanding: Instead of using the 4D-aware mask generation module, the system uses a mask generated solely from the camera trajectory (i.e., simply masking the region where the object would be placed without accounting for scene geometry or occlusions). The mask does not respect occlusion relationships. Quantitatively (Table 3), this configuration achieves CLIP-I 0.7585, DINO-I 0.4190, VBench Background Consistency 0.9206, Subject Consistency 0.9316, Motion Smoothness 0.9910, Imaging Quality 0.6175, and Multi-View Consistency 0.5238. The critical failure mode is that "the model fails to maintain the original video when occlusion occurs... information such as the position of the person's arm and scarf [is lost] compared to the source video" (supplementary text). The mask incorrectly covers scene elements that should remain visible (or fails to cover the object when it should be visible behind an occluder), causing the generation model to overwrite content it should preserve or fail to generate the object where it should appear.

Configuration (b) — 4D-aware mask without first-frame inpainting: Adding the 4D geometry-aware mask sequence (mask generation module active) "significantly addresses such occlusion issues" (supplementary text). Quantitatively (Table 3): CLIP-I 0.7532, DINO-I 0.3861, VBench Background Consistency 0.9232, Subject Consistency 0.9380, Motion Smoothness 0.9913, Imaging Quality 0.6298, Multi-View Consistency 0.5308. Compared to configuration (a), Background Consistency improves (+0.0026), Subject Consistency improves (+0.0064), Imaging Quality improves (+0.0123), and Multi-View Consistency improves (+0.0070). These are consistent but small improvements on VBench metrics. However, the paper notes that despite the improved mask, "the object's fidelity remains low" — CLIP-I actually decreases slightly (-0.0053) and DINO-I drops substantially (-0.0329). The implication: 4D-aware masks fix occlusion-related spatial errors (which VBench's consistency metrics capture) but do not improve object identity preservation, which requires better per-frame generation quality.

Configuration (c) — Adding first-frame inpainting: The first-frame inpainting strategy (OmniPaint for frame 1, video model for remaining frames) is activated. Quantitatively (Table 3): CLIP-I 0.7880, DINO-I 0.5135, VBench Background Consistency 0.9175, Subject Consistency 0.9290, Motion Smoothness 0.9911, Imaging Quality 0.6318, Multi-View Consistency 0.5436. The key changes: DINO-I jumps from 0.3861 to 0.5135 (+0.1274, a 33% relative improvement), and CLIP-I improves from 0.7532 to 0.7880 (+0.0348). This confirms that the image inpainting model produces a first frame with substantially higher object structural fidelity than the video model alone, and that the video model successfully propagates this fidelity through time. However, Background Consistency decreases slightly (0.9232 → 0.9175), suggesting a potential tradeoff: the improved object quality may come at the cost of slightly less faithful background preservation, or the model overfits to the first frame in ways that create minor background inconsistencies. The supplementary text notes: "temporal inconsistencies still arise after occlusion events, manifesting as fluctuations in attributes like scarf direction or surface texture" — indicating that while object identity is better preserved, the model still struggles with maintaining consistent appearance of scene elements (not the inserted object) after occlusions, likely because it has not been trained to understand the physical relationship between the object and surrounding lighting.

Full model — Adding ROSE++ LoRA fine-tuning: The video generation model is fine-tuned on the ROSE++ dataset using LoRA (rank 128). Quantitatively (Table 3): CLIP-I 0.8122, DINO-I 0.5678, VBench Background Consistency 0.9429, Subject Consistency 0.9520, Motion Smoothness 0.9916, Imaging Quality 0.7101, Multi-View Consistency 0.5857. Compared to configuration (c): CLIP-I improves by +0.0242, DINO-I by +0.0543, Background Consistency by +0.0254, Subject Consistency by +0.0230, and Imaging Quality by +0.0783. The improvement in Imaging Quality (+0.0783, ~12.4% relative) is particularly notable — this metric captures overall visual quality including artifacts, blur, and unnatural appearance, suggesting that ROSE++ fine-tuning produces qualitatively more natural-looking videos.

Illumination adaptation (LoRA fine-tuning on ROSE++): Figure 6(a) provides a qualitative ablation of ROSE++ training for illumination changes. In a door opening/closing sequence, the non-fine-tuned model produces a paper bag with constant brightness regardless of whether the door is open (sunlight present) or closed (darkness). After ROSE++ fine-tuning, "the illumination on the object dynamically responds to the scene lighting, the bag appears brighter when sunlight enters through the open door and darker when the door is closed." This is not captured by the automated metrics (which do not explicitly test illumination consistency) but is visually salient and contributes to the user study's high Lighting Consistency preference (71.82%).

Shadow generation (LoRA fine-tuning on ROSE++): Figure 6(b) provides a qualitative ablation for shadow synthesis. Without ROSE++, "the model fails to infer that shadows should naturally extend beyond the object mask, resulting in behavior similar to a simple inpainting model." After ROSE++ fine-tuning, "the model learns to infer the global light direction and intensity, allowing realistic shadows to be synthesized on surrounding surfaces outside the mask." This capability directly stems from the ROSE++ training paradigm where the loss is computed over the entire frame — the model is explicitly penalized for missing shadows outside the mask region, unlike self-supervised inpainting which only trains on masked-region reconstruction.

VLM-based object retrieval vs. random frame crops: Table 2 compares models trained with different reference image generation methods, evaluated on Multi-View Consistency. The model trained with VLM-generated reference images (Ours) achieves 0.5857. The model trained with object crops taken directly from random frames of the ROSE videos (Ours_random) achieves 0.5295. The difference is +0.0562 (+10.6% relative). This ablation verifies the paper's claim that training with video-frame crops causes copy-paste artifacts due to training-inference distribution mismatch: during training, the model sees reference images that contain video-specific lighting, background fragments, and perspective, which do not match the clean white-background reference images used at inference time. The VLM-generated references eliminate this mismatch, resulting in better generalization to clean reference images. The paper's supplementary text (Table 2 caption) explains: "If the model were to depend on such object directly from video, copy-and-paste behavior would occur, leading to inconsistent object appearances across different viewpoints."

Scene flow based object propagation: Supplementary Figure 7 provides a qualitative ablation of the scene flow mechanism. Without scene-flow-based propagation, an object placed on a moving cart stays at its initial 3D position while the cart moves forward — "the version with a static object mask fails to capture the object's motion on the moving cart, resulting in misaligned masks." With scene flow, "the object is naturally placed on top of the cart across time." No quantitative metrics are reported for this ablation specifically — the comparison is purely visual and confirms that without scene flow, dynamic scene interactions produce geometrically incorrect masks that would propagate errors to the final generated video. The first frame is identical in both variants (since scene flow only affects subsequent frames), so the failure manifests as increasingly misaligned masks over time.

Ablation results summary. The sequential ablation reveals a clear additive pattern: 4D-aware masks primarily improve occlusion handling and multi-view consistency metrics (+6.8% on Multi-View Consistency from (a) to (b)); first-frame inpainting primarily improves object identity preservation (+33% on DINO-I from (b) to (c)); ROSE++ fine-tuning improves both overall video quality (+12.4% on Imaging Quality) and background preservation (+2.8% on Background Consistency) while further boosting subject fidelity. The largest single jump is the DINO-I improvement from adding first-frame inpainting, confirming the paper's claim that image models currently substantially outperform video models on per-frame object generation fidelity. The ROSE++ fine-tuning provides the second-largest gains and uniquely enables photometric effects (shadows, illumination changes) that are absent from all prior configurations.

Critical Assessment

Does the paper demonstrate that InsertAnywhere achieves "geometrically consistent object placement and appearance-faithful video synthesis"?

The experimental evidence partially supports this claim, with important caveats.

On geometric consistency: The Multi-View Consistency metric (0.5857 for InsertAnywhere vs. 0.5439 for Kling, Table 1) and the Occlusion Integrity user preference (86.67%, Table 4) provide positive evidence. The qualitative examples (Figure 4, pepper shaker occlusion; supplementary Figure 7, cart tracking) show specific cases where InsertAnywhere handles occlusions and dynamic objects that the baselines mishandle. However, the experimental design provides only indirect evidence for the geometric accuracy of the 4D mask generation. No quantitative evaluation of mask accuracy is reported — there is no comparison of generated masks against ground-truth occlusion boundaries, no metric for mask temporal consistency, no measurement of placement accuracy relative to user specification. The claim of "geometrically consistent" placement rests on the downstream video quality metrics and user preferences, which conflate mask quality with generation quality. It is possible that the masks are moderately accurate and the diffusion model compensates for inaccuracies during synthesis — but the paper provides no way to disentangle these effects. A direct mask accuracy evaluation (e.g., comparing projected object positions against manually annotated ground-truth masks across frames, measuring IoU or boundary consistency) would substantially strengthen this claim.

On appearance faithfulness: The CLIP-I (0.8122) and DINO-I (0.5678) metrics (Table 1) show strong object identity preservation relative to the baselines. The first-frame inpainting ablation (DINO-I increase from 0.3861 to 0.5135 between configurations (b) and (c), Table 3) provides causal evidence that the image inpainting anchor frame is responsible for much of this fidelity. However, "appearance-faithful" is measured only as similarity to the reference image, which is a single 2D view. The reference image shows the object from one angle under one lighting condition — the generated video necessarily shows the object from multiple viewpoints under varying scene lighting. A CLIP-I of 0.8122 indicates strong but not perfect fidelity; the remaining gap to 1.0 could reflect either genuine viewpoint-dependent appearance changes (which are correct and desirable — the object should look different from the side than from the front) or identity drift (which is an error). The paper does not distinguish between these cases. A more rigorous evaluation would include multi-view reference images and measure whether viewpoint-dependent appearance changes follow physically plausible patterns rather than arbitrary drift.

Does the paper demonstrate that InsertAnywhere "significantly outperforms existing research and commercial models, achieving VOI quality suitable for production environments"?

The claim of outperforming commercial models is supported by the automated metrics (Table 1) and user study (Table 4). The claim of outperforming "existing research" is not supported, because no research methods (INVI [21], VideoAnyDoor [25], GenProp [14], DreamInsert [32]) are included in the quantitative comparison. The related work section critiques these methods, but the experiments only evaluate against two commercial tools. This is a significant omission — the paper's central claim in the abstract and conclusion is relative to "existing research and commercial models," but the evidence only covers the commercial half.

The claim of "VOI quality suitable for production environments" is not rigorously supported. The user study provides some evidence (70% overall naturalness preference, Table 4), but it evaluates only 10% of test videos (~10 samples) with 20 participants — the sample sizes for both videos and participants are small. No professional video editors or VFX artists were involved (or at least none are reported as such). Production environments have requirements beyond perceptual naturalness: consistent behavior across edge cases, predictable failure modes, integration with existing post-production pipelines, and measurable quality guarantees. The paper provides no robustness analysis (how does performance degrade with lower-quality source videos? with unusual camera motions? with complex multi-object scenes?), no failure case analysis (what types of videos does it fail on?), and no discussion of practical deployment considerations (processing time per video, GPU requirements, handling of videos longer than 81 frames). The "production environments" claim is aspirational rather than demonstrated.

Does the paper demonstrate that the 4D-aware mask generation module handles "complex camera motions and occlusion"?

The qualitative evidence (Figure 4, Figure 7, Figures 9-13) shows successful cases. The Multi-View Consistency metric (Table 1) and the occlusion handling ablation (supplementary text) provide supporting quantitative evidence. However, the evidence is limited in several ways:

No systematic occlusion benchmark. VOIBench is described as covering "indoor scenes, outdoor environments, and natural landscapes" with "contextually relevant objects," but no statistics are provided on what fraction of videos contain occlusions, how severe those occlusions are, or how many frames are affected. The Multi-View Consistency metric evaluates occlusion handling, but only at the aggregate level — we do not know whether InsertAnywhere handles 90% of occlusion events correctly or only 60%, because no per-event breakdown is provided.
No diversity analysis for camera motion. The paper claims to handle "complex camera motions," but VOIBench does not appear to be stratified by motion type (static camera, panning, handheld, dolly, etc.). Without such stratification, we cannot assess whether the 4D reconstruction pipeline degrades on particular motion patterns (e.g., rapid camera movement causing motion blur that degrades depth estimation, or purely rotational motion that challenges structure-from-motion).
The reconstruction pipeline is inherited, not validated. The mask generation builds on Uni4D [30], which orchestrates multiple pretrained models. The paper does not report any accuracy metrics for the depth estimation, optical flow, or camera pose recovery on VOIBench videos. If depth estimation fails on a particular scene, the entire mask propagation will be wrong, and the generated video will have geometrically incorrect object placement — but the paper provides no way to know how often this occurs or whether it explains any failure cases.

Does the ROSE++ dataset genuinely enable illumination-aware synthesis, or are the improvements attributable to other factors?

The ablation evidence (Figure 6, Table 3) shows that adding ROSE++ LoRA fine-tuning produces improvements on Background Consistency (+0.0254) and Imaging Quality (+0.0783) beyond what first-frame inpainting alone provides. The qualitative examples (Figure 6) show illumination adaptation and shadow generation that are visually compelling. However, several questions remain:

Is the improvement specific to illumination, or is it a general quality improvement from additional fine-tuning? ROSE++ fine-tuning adds 5,000 iterations of domain-specific training on top of the pretrained VACE model. It is possible that any domain-relevant fine-tuning (even without the illumination-aware aspect of ROSE++) would produce quality improvements simply by adapting the model to the VOI task distribution. An informative ablation would be: train on ROSE++ but with the loss computed only within the masked region (i.e., standard inpainting loss), and compare against the full-frame loss version. This would isolate whether the full-frame loss (the key innovation) specifically causes the illumination improvements or whether they arise from other aspects of the ROSE++ training.
The ROSE dataset is synthetic. ROSE [15] consists of rendered 3D scenes with physically simulated lighting. The paper provides no analysis of whether the model trained on synthetic lighting effects generalizes to real-world lighting conditions. Real-world shadows have soft penumbras, colored light bleeding, and complex multi-source illumination that may differ from the simplified lighting models used in the ROSE renderer. If ROSE's lighting is systematically simpler than real-world lighting, the model may learn to generate shadows and reflections that look correct for synthetic scenes but appear subtly wrong in real footage — a discrepancy that generic video quality metrics would not necessarily capture but that human viewers might notice.
The VLM-generated references may introduce artifacts. The reference images are generated by a VLM (likely GPT-4's image generation) from multi-view object crops. If the VLM misinterprets the object's geometry or texture, the generated reference will not perfectly match the object as it appears in the target video. During training, this creates a noisy supervision signal — the model is told to insert an object matching a reference image that is itself an imperfect approximation. The DINO-based selection (Equation 4) mitigates this by choosing the best-matching candidate, but it does not guarantee perfect fidelity. The paper does not report the DINO similarity between selected VLM-generated references and the ground-truth object appearances, so the magnitude of this noise is unknown.

Missing experiments and evaluations that would strengthen the paper

Several experiments would address the limitations identified above:

Direct mask accuracy evaluation. Generate masks on a subset of VOIBench with manual annotations of object boundaries and occlusion events. Report IoU, boundary F1, and temporal consistency metrics for the masks against ground truth. Compare against naive mask propagation baselines (e.g., tracking the mask from frame 1 using optical flow directly, without 4D reconstruction).
Research method baselines. Include at least one open-source VOI method (e.g., VideoAnyDoor [25], INVI [21], or GenProp [14]) in the quantitative comparison. This would validate the claim of outperforming "existing research" and provide a more complete picture of where InsertAnywhere stands relative to the academic state-of-the-art.
Failure case analysis. Report examples where InsertAnywhere fails (object identity drift, incorrect occlusion handling, missing or unrealistic shadows, temporal flickering). Characterize the conditions under which failures occur (rapid camera motion? complex lighting? thin or transparent objects? highly textured backgrounds?). This is essential for assessing the "production environments" claim.
Synthetic-to-real generalization study. Train on ROSE++ only and evaluate on ROSE++ test split vs. real-world VOIBench. Compare performance to see if there is a significant domain gap. If the gap is large, the synthetic training data strategy may be less effective than claimed.
Ablation isolating full-frame loss. Train a variant on ROSE++ where the diffusion loss is computed only within the mask region (standard inpainting loss) and compare against the full-frame loss variant on real-world videos. This would directly test whether the full-frame loss is the causal mechanism for illumination synthesis improvements.
4D reconstruction component analysis. On VOIBench, measure how often the depth estimation, optical flow, and camera pose recovery produce reasonable results (e.g., depth maps that are consistent with known scene geometry, flow fields that are temporally coherent). Report how downstream VOI quality correlates with component model accuracy. This would help practitioners understand which component models are the bottlenecks and where future improvements should focus.
Computational cost analysis. Report the wall-clock time and GPU memory requirements for each stage (4D reconstruction, mask generation, first-frame inpainting, video diffusion) on representative hardware. Compare against the inference time of commercial baselines (to the extent measurable through API latency). The "production environments" claim requires practical deployability, which cost analysis directly addresses.

Summary of critical assessment

The experiments in this paper provide strong evidence for the relative superiority of InsertAnywhere over Pika-Pro and Kling on the VOIBench benchmark, with the user study corroborating the automated metrics. The ablation study provides compelling causal evidence that the two-stage decomposition (4D masks + illumination-aware synthesis) is responsible for the performance gains, with each component contributing to distinct, non-overlapping improvements. The ROSE++ dataset construction is innovative and its contribution is validated by the ablation, though the specific mechanism (full-frame loss vs. general fine-tuning effects) is not fully isolated.

However, the paper's broader claims — outperforming all existing research, achieving production-ready quality, and handling complex real-world scenarios — are not fully validated by the reported experiments. The absence of research baselines, the small scale of the user study, the lack of failure case analysis, and the reliance on synthetic training data without real-world generalization analysis are significant gaps. The paper demonstrates a promising approach to VOI that addresses well-identified limitations of prior work, but the experimental validation is at the proof-of-concept level rather than the production-validation level. The key architectural innovations (4D reconstruction for mask propagation, inverted removal-to-insertion training) are supported by the evidence, but their robustness and scalability to diverse real-world conditions remain open questions.

6. Limitations and Trade-offs

Limitation 1: Difficulty Estimation Cost Dominates the Compute Savings

The assumption or constraint. The paper's compute-optimal framework depends on knowing each prompt's difficulty before allocating the strategy. The authors estimate difficulty by generating 2,048 samples per question and averaging either ground-truth correctness (oracle) or the PRM's final-answer score (predicted). They acknowledge this cost explicitly in Section 3.2:

"estimating difficulty in this way still incurs additional computation cost during inference... our experiments do not account for this cost largely for simplicity"

The consequence. The reported 4× efficiency gains over best-of-N are computed after difficulty is known, without amortizing the cost of learning it. Generating 2,048 samples per question to estimate difficulty consumes more compute than the largest test-time compute budgets studied in the experiments (256–512 generations maximum, Section 5.3). In a real deployment, the total cost would be difficulty_estimation_cost + strategy_execution_cost, and the former dominates the latter — potentially reversing the claimed efficiency advantage. A user deciding to deploy this system would actually pay more total compute than a standard best-of-256 baseline when the difficulty estimation overhead is included, because best-of-256 costs 256 generations while the difficulty estimation alone costs 2,048 generations before the strategy even begins.

What evidence exists in the paper. This limitation is visible in the experimental design itself: Figures 4 and 8 plot accuracy vs. generation budget (up to 256–512), with the difficulty estimation cost entirely excluded from the x-axis. The predicted difficulty variant still uses the same 2,048-sample estimation pipeline — it simply substitutes the PRM's prediction for ground-truth correctness labels. The 4× efficiency claim (Section 5.3: "16 generations matching 64") implicitly assumes difficulty is known for free, which is not the case in the proposed system.

Mitigation status. The paper flags this explicitly as a limitation and suggests future work on "pretraining or finetuning models to directly predict difficulty of a question" (Section 8). No such model is developed or evaluated in the paper. The authors also mention the possibility of adaptive difficulty estimation — starting with a few samples and adjusting strategy mid-computation — but do not explore this. The limitation is currently unmitigated; the 4× figure should be understood as an upper bound on achievable efficiency once cheap difficulty estimation is available.

Limitation 2: Hard Problems Show Near-Zero Improvement Regardless of Budget

The assumption or constraint. The compute-optimal framework assumes that test-time compute can meaningfully improve performance if allocated correctly. However, the paper's difficulty bin 5 (hardest questions) consistently shows that no amount of test-time compute helps when the base model's pass@1 is near zero.

The consequence. On the hardest problems (bin 5, approximately 20% of the MATH test set), accuracy remains at 1–3% regardless of budget or strategy — search, revisions, and compute-optimal allocation all fail equally (Figure 3 right, Figure 7 right, Figure 9 bottom lines). The paper acknowledges this explicitly (Section 7 takeaway box) but the consequence for deployment is significant: test-time compute cannot create capability that does not already exist in the base model. If a problem is genuinely outside the model's knowledge or reasoning range, no amount of resampling, beam search, or iterative revision will solve it. For a deployment where the problem distribution contains a non-trivial fraction of such hard problems (e.g., competition-level math, novel reasoning tasks), test-time compute strategies provide zero benefit, and the only path to improvement is scaling pretraining.

What evidence exists in the paper. Figure 3 (right) shows bin 5 accuracy at 1–3% for all methods across all budgets (4 to 256 generations). Figure 7 (right) shows bin 5 accuracy at 2–3% for all sequential-to-parallel ratios at 128 generations. Figure 9 shows the bin 5 scaling curve essentially flat near 0–5%, with both revisions and PRM search failing to improve over the base model's random performance. The FLOPs-matched comparison (Figure 1, bottom-right bar chart) quantifies this: on hard problems at R ≫ 1 with PRM search, test-time compute shows a −52.9% relative disadvantage compared to the ~14× larger pretrained model.

Mitigation status. The paper is transparent about this limitation — the Section 7 takeaway explicitly states: "test-time compute can amplify existing capability but cannot create it." However, the paper does not explore whether this boundary can be pushed (e.g., through better verifiers, different base model architectures, or combining with retrieval). The limitation is acknowledged but unresolved — it defines a hard capability ceiling for all test-time compute methods that the paper's framework cannot cross.

Limitation 3: Sequential Revisions Introduce Latency That the Compute Budget Model Ignores

The assumption or constraint. The paper's "generation budget" — measured as number of complete solutions sampled — serves as a proxy for total compute cost. This budget model treats all generations as occupying equivalent FLOPs, which is approximately correct. However, it ignores wall-clock latency: sequential revisions are inherently serial (each revision depends on the previous one), while parallel best-of-N can be executed simultaneously given sufficient hardware.

The consequence. The compute-optimal policy often favors sequential strategies, particularly on easy prompts. For revisions, the optimal sequential-to-parallel ratio on easy problems skews toward fully sequential (Figure 7, right, bin 1), and on the aggregated data, sequential-only outperforms parallel-only (Figure 6, right: 41.5% vs. 39% at 64 generations). A fully sequential chain of 64 revisions takes 64× the wall-clock time of 64 parallel samples, assuming the parallel samples can be batched. For latency-sensitive applications — interactive assistants, real-time decision-making systems, or any deployment where users are waiting for a response — the sequential-heavy strategies favored by compute-optimal allocation on easy problems may be practically unusable regardless of their accuracy advantages. A practitioner deploying this system would face a tradeoff between accuracy (compute-optimal sequential revisions) and latency (parallel best-of-N) that the paper's generation-budget metric completely obscures.

What evidence exists in the paper. This limitation is visible in the revision model's design (Section 6.1): the model generates a chain of revisions sequentially, with each step conditioning on the previous output. Figure 6 (left) shows the pass@1 trajectory across up to 64 sequential revisions — each step adds one serial generation to the latency budget. The sequential-to-parallel ratio sweeps (Figure 7) never discuss latency implications. No wall-clock time measurements are reported anywhere in the paper.

Mitigation status. The paper does not acknowledge or discuss this tradeoff. The generation budget metric is used throughout as the sole cost measure, and the compute-optimal policy is selected to maximize accuracy per generation, not accuracy per second. A practitioner would need to independently evaluate whether the accuracy gains from sequential strategies justify the latency cost in their specific deployment context. This is a fundamental design tension that the paper does not address: the same strategies that are compute-optimal (in FLOPs) may be latency-suboptimal (in wall-clock time).

Limitation 4: Single Benchmark and Single Model Family Limits Generalization Claims

The assumption or constraint. All experiments use the MATH benchmark (500 test questions) with PaLM 2-S* as the base model. The paper's findings about difficulty-dependent strategy effectiveness, verifier over-optimization, and the 4× efficiency gains are derived entirely from this single benchmark-model combination. The authors state that they "believe this model is representative of the capabilities of many contemporary LLMs" (Section 4), but this belief is untested within the paper.

The consequence. Several critical findings could be specific to the MATH + PaLM 2-S* combination rather than general properties of test-time compute scaling:

Verifier over-optimization behavior (beam search degrading on easy problems, Figure 3 right) depends on the PRM's calibration, which in turn depends on the base model's output distribution. A model with different error patterns (e.g., a model that makes different types of mistakes on easy problems) might exhibit different over-optimization thresholds, changing the optimal allocation policy.
Revision model effectiveness (Figure 6, sequential outperforms parallel) depends on the base model's in-context learning and self-correction capabilities, which vary across model families and sizes.
The 4× efficiency gain is specific to the performance range of PaLM 2-S* on MATH (approximately 10–19% pass@1, reaching ~40% with compute-optimal scaling). A stronger base model with higher initial pass@1 might saturate sooner, reducing the room for test-time compute to provide gains. A weaker base model might not benefit at all.
Task specificity: MATH consists of competition-level symbolic math problems. Whether the difficulty-dependent patterns (easy = sequential revisions help, medium = beam search helps, hard = nothing helps) generalize to code generation, logical reasoning, scientific QA, or tasks requiring factual recall is unknown. The MATH benchmark's discrete, verifiable answers also enable the clean Monte Carlo rollout PRM training — tasks without such clean correctness signals would require entirely different verifier training approaches.

What evidence exists in the paper. All quantitative results (Figures 3, 4, 6, 7, 8, 9; Tables 1) are reported on the MATH test set with PaLM 2-S*. No results are reported on any other benchmark (e.g., GSM8K, HumanEval, MMLU) or with any other model family (e.g., LLaMA, Claude, GPT). The PRM training uses PaLM 2-S* outputs exclusively; the revision model is a PaLM 2-S* fine-tune. The ~14× larger model used in the FLOPs-matched comparison is also from the PaLM 2 family.

Mitigation status. The authors acknowledge the single-model limitation implicitly by stating their model is "representative," but do not conduct any cross-model or cross-benchmark experiments to validate this claim. The limitation is unmitigated — a practitioner considering deploying this approach with a different model (e.g., a LLaMA variant) or on a different task (e.g., code generation) would need to replicate the entire analysis pipeline (PRM training, revision model training, difficulty binning, strategy selection, FLOPs matching) to determine whether the findings transfer.

Limitation 5: The Revision Model Has a 38% Correct-to-Incorrect Reversion Rate

The assumption or constraint. The revision model is trained on trajectories where all in-context answers are incorrect, followed by a correct target (Section 6.1). This training data construction — pairing independently sampled correct and incorrect solutions post-hoc using edit distance to ensure structural similarity — means the model never sees examples where the current answer is already correct and should be preserved.

The consequence. At inference time, when the revision chain produces a correct answer at some step, the model has no training signal for what to do — it only knows how to revise incorrect answers into correct ones, not how to recognize that no revision is needed. The paper reports that approximately 38% of correct answers get converted back to incorrect ones in the subsequent revision step (Section 6.1). This means that a revision chain of length N does not monotonically improve — correct answers can be "un-corrected" by later revisions, and the final answer in the chain is not necessarily the best one. The system mitigates this by selecting the best answer from any point in the chain (via majority voting or verifier-based selection), but this is a patch rather than a solution: it means that a substantial fraction of the sequential generation budget is wasted on revisions that actively degrade quality.

What evidence exists in the paper. The 38% reversion rate is reported directly in Section 6.1: "approximately 38% of correct answers get converted back to incorrect ones using a naive approach." The within-chain selection mechanism (using majority voting or verifier-based selection across all revision steps) is described as the mitigation. Figure 6 (left) shows that pass@1 at each step gradually improves throughout the chain (from ~18.2% at step 1 to ~24-25% by step 15-20), indicating that the net effect of revisions is positive despite reversions — but the curve would presumably be steeper if reversions could be prevented.

Mitigation status. The paper acknowledges this issue and implements a within-chain selection mechanism to mitigate it (selecting the best answer from any step rather than always taking the last revision). However, the mitigation is imperfect: the selection mechanism can only choose among answers that were actually generated; it cannot prevent a correct answer from being lost if no later step recovers it. The paper does not explore more principled solutions, such as training the revision model to recognize correct answers and output a "no revision needed" token, or training with mixed trajectories that include correct-in-context examples. The ReST $^{EM}$ experiment (Appendix K, Figure 16) shows that an alternative training approach actually worsened the problem, suggesting the revision training is fragile. This limitation is partially mitigated but not resolved — the 38% reversion rate represents a fundamental efficiency loss in the sequential revision pipeline that scales with chain length.

Limitation 6: The `14×` Larger Model Baseline Is Not Compute-Optimal

The assumption or constraint. The FLOPs-matched comparison in Section 7 scales model parameters by ~14× while keeping training data fixed, following the LLaMA paradigm (parameter scaling only). The authors acknowledge that this departs from compute-optimal pretraining (Hoffmann et al., 2022), where both parameters and data are scaled equally:

"We choose this setting as it is representative of a canonical approach to scaling pretraining compute and leave the analysis of compute-optimal scaling of pretraining compute where the data and parameters are both scaled equally to future work." (Section 7)

Additionally, the larger model uses only greedy decoding — no majority voting, no best-of-N, no search of any kind.

The consequence. The reported advantages of test-time compute over pretraining — for example, +27.8% relative improvement on easy questions at R ≪ 1 with revisions (Figure 1, top-right bar chart) — may be overstated because the pretraining baseline is suboptimal in two ways:

Non-Chinchilla-optimal training: A model trained with 14× more FLOPs allocated optimally (scaling both parameters and data) would likely achieve higher performance than a parameters-only-scaled model. The paper's pretraining baseline is weaker than a compute-matched optimal model would be, making test-time compute look better by comparison.
No test-time compute for the larger model: The larger model uses greedy decoding with no additional inference budget. A fairer comparison would give the larger model some test-time compute budget as well — for example, best-of-8 or best-of-16 — and compare against the smaller model's compute-optimal allocation. The paper's comparison is effectively "small model with sophisticated inference vs. large model with minimal inference," which conflates the benefits of test-time compute with the benefits of the allocation strategy.

What evidence exists in the paper. The FLOPs-matched analysis is described in Section 7 with the parameter-only scaling assumption explicitly stated. Figure 9 and the bar charts in Figure 1 show the comparison results. The paper does not include any variant where the larger model receives test-time compute, nor does it include a Chinchilla-optimal training baseline.

Mitigation status. The paper acknowledges the deviation from compute-optimal pretraining and frames the parameter-only scaling as "representative of a canonical approach" (Section 7). However, this is a significant caveat to the paper's most striking headline claim — that test-time compute with a small model can outperform a 14× larger model. A practitioner deciding between training a larger model and deploying test-time compute with a smaller one would need to know whether the results hold against a properly compute-optimal larger model. The paper's evidence does not answer this question. Future work on joint optimization of pretraining and inference compute (Section 8) is suggested but not attempted. The limitation is acknowledged but not resolved — the comparison favors test-time compute in ways that may not generalize to optimal pretraining regimes.

7. Implications and Future Directions

How This Work Changes the Landscape

This paper establishes that video object insertion is fundamentally a geometric + photometric composite problem, not a monolithic generation task that can be solved by scaling a single end-to-end model. The key conceptual shift is the demonstration that explicit 4D scene reconstruction — building an actual 3D representation from monocular video using pretrained depth, flow, and segmentation models — serves as a viable and necessary interface layer between user intent and generative video synthesis. Prior work treated VOI as a modeling problem (how to design better inpainting architectures, how to propagate appearance more smoothly), but InsertAnywhere argues that the bottleneck is not generative capability but rather the absence of explicit spatial reasoning. The diffusion model can already generate realistic pixels; what it lacks is the geometric information about where those pixels should go under viewpoint changes and occlusions.

This reframing changes how the field should think about VOI system design. The paper's ablation study (Figure 5, Table 3) provides causal evidence that geometric and photometric improvements address non-overlapping failure modes: better masks fix occlusions but not object fidelity; better per-frame generation fixes fidelity but not missing shadows; better training data fixes shadows but not occlusions. This diagnostic clarity is absent from end-to-end approaches where a quality drop could stem from any combination of geometric, photometric, or temporal causes. The implication for future work is that VOI systems should be evaluated not just by aggregate metrics but by failure-mode-specific diagnostics — if your system produces objects that look realistic but float, the geometry module needs work; if they are perfectly placed but look like cutouts, the photometric module needs improvement. This decomposition into independently measurable capabilities is a methodological contribution that the field would benefit from adopting.

The paper also resolves a tension that was implicit in prior work: why do inpainting methods, propagation methods, and commercial text-guided tools all fail in different ways, and why do their failure patterns appear complementary? The paper's answer is that each method solves only one part of the VOI problem. Inpainting methods provide precise spatial control (masks) but are trained with objectives that cannot capture lighting effects beyond mask boundaries. Propagation methods maintain appearance coherence but lack 4D visibility reasoning, so they break under occlusion. Commercial text-guided tools have strong generative priors from large-scale training but cannot accept precise spatial constraints through language. InsertAnywhere's two-stage architecture demonstrates that combining spatial precision (from 4D masks) with illumination-aware generation (from ROSE++ training) produces videos that none of the individual paradigms can achieve alone. The previously contradictory results — some papers showing VOI "works" and others showing it fails on real scenes — can now be understood as artifacts of which sub-problem each method was designed to solve and which it neglected.

Perhaps most significantly, this paper redirects research attention toward data construction strategies for supervised video editing. The ROSE++ dataset construction — inverting an object-removal dataset and using VLMs to fill missing components — represents a template for building supervised training data for tasks that lack naturally occurring paired examples. The key finding that full-frame loss (not mask-constrained inpainting loss) is necessary for learning lighting effects has implications beyond VOI. Any video editing task where the edit produces photometric changes beyond the edited region — object recoloring with interreflections, adding light sources to scenes, changing weather conditions — would benefit from the same inversion-and-supervision strategy. The bottleneck for these tasks shifts from "how do we design a better architecture" to "can we find or build a dataset where the before/after pair captures the full photometric consequences of the edit." The paper makes this data-centric approach newly tractable by demonstrating that synthetic renderers (like the one used to build ROSE) combined with VLM-based conditioning-signal generation can produce sufficient training data.

The practical consequence for the video editing research landscape is that explicit 3D/4D scene understanding becomes a worthwhile investment rather than a detour. The computer vision community has often treated monocular 4D reconstruction as a standalone challenge evaluated on geometric accuracy metrics. InsertAnywhere shows that even imperfect 4D reconstruction — built from off-the-shelf pretrained models without fine-tuning — is already sufficient to dramatically improve downstream VOI quality when used as spatial conditioning for a generative model. This lowers the barrier for practitioners: you do not need to solve 4D reconstruction to research-grade accuracy before it becomes useful. The generative model's learned visual priors compensate for reconstruction inaccuracies. This finding should accelerate integration between the 3D reconstruction community and the generative video editing community, which have largely operated in isolation.

Follow-Up Research This Work Enables

Direct mask accuracy evaluation and 4D reconstruction bottleneck analysis. The paper's claim of "geometrically consistent" placement rests entirely on downstream video quality metrics (Table 1, Multi-View Consistency 0.5857) and qualitative occlusion examples (Figure 4). A critical missing experiment is a direct evaluation of the generated masks against ground-truth annotations. A strong follow-up would manually annotate object boundaries and occlusion events on a subset of VOIBench (or a new benchmark with controlled camera motions and known occlusion patterns), then measure mask IoU, boundary F1, and temporal consistency for the 4D-aware mask generation module compared against ablations (naive camera-only projection, optical-flow-based mask tracking without 3D reconstruction, per-frame SAM2 prompting). This would isolate how much of the Multi-View Consistency improvement comes from mask quality versus the diffusion model's ability to compensate for mask errors. If mask quality is near-perfect on simple scenes but degrades sharply on rapid camera motion or thin structures, that identifies the reconstruction bottleneck. If mask quality is moderate across the board but final video quality is high, that quantifies the diffusion model's robustness to spatial conditioning noise — a practically important finding for system designers.

Synthetic-to-real generalization study for illumination-aware synthesis. The ROSE++ dataset is synthetic — rendered 3D scenes with physically simulated lighting. The paper evaluates the ROSE++-trained model only on real-world VOIBench videos, providing no direct measurement of the domain gap. A critical follow-up would construct a matched evaluation: hold out a subset of ROSE videos as a synthetic test set, train the model on the remaining synthetic data, and report metrics (CLIP-I, DINO-I, VBench) on both the synthetic holdout and real-world VOIBench. The performance gap between synthetic and real would quantify how much the illumination synthesis capabilities learned from simplified rendered lighting transfer to complex real-world illumination (colored light bleeding, soft penumbras, multi-source ambient occlusion). If the gap is large, it suggests that synthetic training alone is insufficient and that real-world supervised pairs (perhaps from light-stage captures or multi-exposure photography) are needed. If the gap is small, it validates the synthetic data strategy and opens the door to building much larger synthetic VOI datasets with more diverse lighting conditions. This experiment would also reveal whether certain metrics (e.g., Background Consistency) degrade more than others on real data, indicating specific photometric effects that synthetic renderers fail to capture.

Full-frame loss versus mask-constrained loss ablation on real-world generalization. The paper attributes the illumination synthesis improvements of ROSE++ fine-tuning to the full-frame diffusion loss (computed over the entire frame, not just the masked region). However, ROSE++ also provides 5,000 iterations of domain-specific fine-tuning that adapts the pretrained VACE model to the VOI task distribution. It is possible that any domain-relevant fine-tuning — even with a standard mask-constrained inpainting loss — would produce similar quality improvements simply by reducing the distribution shift between pretraining and VOI inference. A clean ablation would train two models: one on ROSE++ with full-frame loss (the paper's current approach) and one on ROSE++ with loss computed only within the mask region (standard inpainting training). If the full-frame-loss model significantly outperforms on shadow generation (qualitatively) and on Background Consistency (quantitatively, since it must learn to preserve distant pixels while modifying nearby ones), it confirms that the full-frame supervision is causally responsible for illumination synthesis. If the two models perform similarly, the gains are attributable to domain adaptation rather than the specific loss formulation, and the paper's central claim about ROSE++'s mechanism is weakened. This experiment would also inform future dataset construction: if full-frame loss is critical, any VOI training dataset must include photometric side effects in the target video, which constrains how datasets can be built.

Combining 4D mask generation with commercial text-guided baselines. The paper's evaluation compares InsertAnywhere against Pika-Pro and Kling as end-to-end alternatives. A natural question is whether the paper's key innovation — 4D-aware masks — could improve the commercial baselines directly. Specifically: take the mask sequence generated by InsertAnywhere's 4D-aware module, provide it (along with the reference image and a text prompt) to Pika-Pro or Kling as an additional conditioning input (if their APIs support mask-based inpainting), and measure whether adding explicit spatial conditioning improves their object fidelity and occlusion handling. If the commercial tools with 4D masks match or exceed InsertAnywhere's performance, it suggests that the paper's contribution is primarily the mask generation module and that the video synthesis component can be outsourced to commercial models — a practically useful finding for practitioners who want the best of both worlds. If the commercial tools cannot effectively use mask conditioning, it reveals a limitation of their architectures and strengthens the case for InsertAnywhere's custom fine-tuned synthesis model. This experiment would also serve as a strong baseline for future work: it establishes whether 4D-aware masks are a plug-and-play improvement to existing systems or whether they require co-designed synthesis models.

Multi-object and dynamic-object insertion with inter-object interactions. InsertAnywhere handles a single static inserted object whose motion is driven by scene flow from nearby surfaces. A natural extension is inserting multiple objects that interact with each other (e.g., a cup and saucer placed on a table, where the cup casts a shadow on the saucer) or inserting dynamic objects with their own independent motion (e.g., a rolling ball that should bounce off scene geometry). The current 4D mask generation module propagates object position via scene flow but does not model object-object interactions or physics-based motion. A follow-up could augment the mask generation stage with simple rigid-body physics (gravity, collision detection against the reconstructed scene geometry, friction with supporting surfaces) to enable autonomous object motion. The key question is how much physical accuracy is needed: do approximate physics produce visually plausible trajectories that the diffusion model can render realistically, or do physically implausible trajectories (even if geometrically consistent with the scene) create uncanny-valley effects that degrade user preference? This experiment would test the limits of the "reconstruction + generation" paradigm — how much physics does the 4D module need to model explicitly, and how much can the diffusion model compensate for?

Failure case taxonomy and difficulty stratification for VOIBench. The paper provides no systematic analysis of when InsertAnywhere fails. A practically important follow-up would categorize VOIBench videos by scene properties that challenge the 4D reconstruction pipeline (rapid camera motion, low-texture surfaces, thin structures like chair legs or plant stems, reflective surfaces, transparent objects, dynamic lighting like flashing lights or moving shadows) and measure per-category performance. This would produce a difficulty taxonomy analogous to the MATH difficulty bins — some scene types may be "easy" for the 4D reconstruction (static camera, Lambertian surfaces, simple geometry) while others are "hard" (fast panning, specular reflections, complex occlusions). Knowing which categories dominate real-world deployment scenarios would tell practitioners whether InsertAnywhere is likely to work on their footage or whether certain scene types require alternative approaches (e.g., multi-view stereo from multiple cameras rather than monocular reconstruction). The taxonomy would also guide future research: if performance drops sharply on thin structures, that motivates better single-view 3D reconstruction for fine geometry; if it drops on dynamic lighting, that motivates training data with more diverse illumination conditions.

Training a difficulty predictor for mask generation quality. The paper's 4D mask generation involves multiple pretrained models (depth, flow, segmentation) whose individual accuracies are unknown on a given video. A practically useful tool would be a lightweight "mask confidence" predictor that takes the source video and the generated mask sequence as input and outputs a frame-by-frame confidence score estimating mask accuracy, without requiring ground-truth annotations. This could be trained on synthetic data where ground-truth masks are available (ROSE videos, or any rendered 3D scene) by measuring actual mask error against generated masks and learning to predict error from observable features (optical flow consistency, depth estimation uncertainty, SAM2 confidence scores). At deployment, low-confidence frames could trigger a fallback strategy (e.g., use a larger mask on those frames to give the diffusion model more freedom, or flag the video segment for manual review). This directly parallels the difficulty estimation problem that the paper flags as a key limitation — the computational cost of assessing mask quality before committing to video generation — and would make the system more robust to reconstruction failures in unpredictable real-world footage.

Practical Applications and Downstream Use Cases

Virtual product placement for advertising at scale. The most immediate application is inserting branded products into existing lifestyle, influencer, or entertainment videos without re-shooting. InsertAnywhere's 79% user preference on Object Realism and 87% on Occlusion Integrity (Table 4) suggest that the output quality is approaching the threshold where inserted objects are not immediately identifiable as synthetic. For an advertising platform, this enables dynamic product insertion: a single video of a kitchen scene could be monetized by inserting different coffee brands for different viewers or markets, with the insertion happening computationally rather than through physical set dressing. The key advantage over commercial tools (Pika-Pro, Kling) is spatial precision — InsertAnywhere places objects at exact user-specified 3D locations, ensuring the product appears on the correct surface at the correct scale, which is critical for brand compliance (a soda can must not float or appear at an implausible size). The 80.91% user preference on Background Preservation (Table 4) is particularly relevant: advertisers need the original video content (the influencer, the set, the product being promoted) to remain intact, which text-guided tools often corrupt through unintended background edits.

Film and video post-production for set extension and prop replacement. In film VFX, inserting objects that were absent during filming — or replacing practical props with digital alternatives — is a common but labor-intensive task typically requiring manual rotoscoping, 3D tracking, and compositing per frame. InsertAnywhere's 4D-aware mask generation automates the 3D tracking and occlusion handling that would otherwise be done manually. The paper's scene flow propagation (supplementary Figure 7) handles cases where the insertion surface moves (e.g., an object on a moving cart), which is a scenario that challenges traditional camera-tracking-only approaches. The key practical benefit is the reduction in manual keyframing: the VFX artist places the object once in the 3D reconstruction and the system propagates placement across the entire shot, with occlusions handled automatically. The first-frame inpainting strategy (OmniPaint for frame 1, video model for subsequent frames) provides a workflow where the artist can verify and adjust the first frame's appearance before committing to the full video generation, enabling iterative refinement without re-rendering the entire sequence.

E-commerce video generation with dynamic product visualization. Online retailers increasingly use video to showcase products, but filming every product variant in every setting is cost-prohibitive. InsertAnywhere enables a workflow where a single base video of a room or setting is filmed once, and different products (furniture, appliances, decor) are inserted computationally to generate product-specific videos. The Illumination Adaptation capability (Figure 6a, paper bag responding to door opening/closing) is critical here: if an inserted sofa fails to darken when curtains are drawn, viewers perceive the video as fake. The ROSE++ training (full-frame loss, Figure 6b shadow generation) specifically enables the model to synthesize realistic shadows and reflections of the inserted product on surrounding surfaces. For an e-commerce platform, this means a single room video can generate hundreds of product-specific videos where each inserted item casts appropriate shadows on the floor and walls, responds to ambient lighting changes, and appears at the correct scale relative to known room geometry — all from a single reference product photo. The CLIP-I score of 0.8122 (Table 1) indicates that the inserted product maintains strong visual fidelity to the reference image, which is essential for accurately representing product color and texture.

When to Prefer This Method

The paper explicitly positions InsertAnywhere against three alternative VOI paradigms, each with specific failure modes that the two-stage architecture addresses. The decision rules emerge directly from the paper's analysis:

Prefer InsertAnywhere over text-guided commercial tools (Pika-Pro, Kling) when precise spatial control and object fidelity are required. The paper's experiments (Figures 4, 9, 10, 11, 12, 13) show that text-guided tools produce objects at incorrect scales, in physically implausible locations, and with degraded visual fidelity (Pika-Pro CLIP-I 0.4940 vs. InsertAnywhere 0.8122, Table 1). If the application requires the object to appear at an exact location, orientation, and scale relative to scene geometry (e.g., product placement, architectural visualization), text-based interfaces are systematically inadequate. The tradeoff is that InsertAnywhere requires a 3D placement GUI and 4D reconstruction preprocessing, adding setup time compared to typing a text prompt.
Prefer InsertAnywhere over mask-constrained inpainting methods when the inserted object produces photometric effects beyond its boundaries (shadows, reflections, illumination changes). The paper's ROSE++ training with full-frame loss (Figure 6, Table 3) enables synthesis of lighting effects extending beyond the object mask, which inpainting methods trained with mask-only reconstruction loss fundamentally cannot produce. If the scene has strong directional lighting where missing shadows would be visually obvious, inpainting methods produce objects that appear "detached" from the scene. The tradeoff is that ROSE++ fine-tuning requires synthetic training data (or its equivalent) and the model may not generalize perfectly to lighting conditions not represented in the synthetic renderer.
Prefer InsertAnywhere over propagation methods (GenProp) when the scene contains occlusions or visibility changes. The 4D-aware mask generation (Figure 4, pepper shaker occlusion; supplementary Figure 7, cart tracking) explicitly handles cases where the inserted object moves behind foreground elements, which propagation methods that rely on 2D appearance propagation cannot manage. The paper's Multi-View Consistency score (0.5857, Table 1) directly measures this capability. The tradeoff is that InsertAnywhere requires sufficient visual features for the 4D reconstruction pipeline to estimate depth and camera motion — scenes with low texture, rapid motion blur, or reflective surfaces may degrade reconstruction quality and produce inaccurate masks. No quantitative boundary on reconstruction failure modes is provided in the paper, so practitioners should evaluate on their specific footage type.

Dependency tree

Papers this one builds on, walking backwards through the same domain and shared topics.

Infinite-Homography as Robust Conditioning for Camera-Controlled Video Generation2025· vision← in-domain predecessor
- Native and Compact Structured Latents for 3D Generation2025· vision← in-domain predecessor

What it influenced

Later papers in the same chain. Direction reversed.

TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times2025· inference-optimization→ LLM-judged similar
- FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space2026· architecture→ LLM-judged similar
- DreamStyle: A Unified Framework for Video Stylization2026· diffusion→ LLM-judged similar
- ArcFlow: Unleashing 2-Step Text-to-Image Generation via High-Precision Non-Linear Flow Distillation2026· diffusion→ LLM-judged similar
- Transition Matching Distillation for Fast Video Generation2026· inference-optimization→ LLM-judged similar
- Plenoptic Video Generation2026· vision→ LLM-judged similar
- FlowAct-R1: Towards Interactive Humanoid Video Generation2026· vision→ LLM-judged similar
- Kascade: A Practical Sparse Attention Method for Long-Context LLM Inference2025· inference-optimization→ in-domain predecessor
- Efficient Autoregressive Video Diffusion with Dummy Head2026· inference-optimization→ similar topic
- Accelerating Speculative Decoding with Block Diffusion Draft Trees2026· inference-optimization→ similar topic
StoryMem: Multi-shot Long Video Storytelling with Memory2025· vision→ in-domain predecessor
- Quantile Rendering: Efficiently Embedding High-dimensional Feature on 3D Gaussian Splatting2025· vision→ in-domain predecessor
- DC-VideoGen: Efficient Video Generation with Deep Compression Video Autoencoder2025· vision→ similar topic
- Towards Universal Video Retrieval: Generalizing Video Embedding via Synthesized Multimodal Pyramid Curriculum2025· vision→ similar topic
- EgoX: Egocentric Video Generation from a Single Exocentric Video2025· vision→ similar topic
SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restoration2025· vision→ similar topic
- SphereDiff: Tuning-free 360° Static and Dynamic Panorama Generation via Spherical Latent Representation2025· vision→ in-domain predecessor
- Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows2021· vision→ similar topic
- SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training2025· vision→ similar topic