TL;DR
VOID extends video object removal to causal interactions where removing objects affects other scene elements. Using counterfactual training, quadmask conditioning, and VLM-guided inference, it outperforms baselines on 105 videos, correctly modeling physics like falling objects and floating balloons.
VOID extends video object removal to causal interactions where removing objects affects other scene elements. Using counterfactual training, quadmask conditioning, and VLM-guided inference, it outperforms baselines on 105 videos, correctly modeling physics like falling objects and floating balloons.
39
已证实
0
证据不足
1
无法验证
N/A
可复现性
核心问题
How can video object removal be extended to handle complex causal interactions where removing an object affects other objects in the scene, such as support removal causing falls or released objects floating?
核心方法
{'approach': 'The framework generates counterfactual video pairs using Kubric simulations (~1900 pairs) and HUMOTO motion capture data (~4500 pairs), trains a CogVideoX-based model with quadmask conditioning that extends trimask to four colors for better guidance, and employs VLM-guided inference with optional two-pass refinement using flow-warped noise stabilization.', 'key_components': ['The problem is formally defined as generating counterfactual videos with objects and their interactions removed.', 'The model must eliminate target objects, regenerate affected regions, and preserve unaffected regions.', 'Complex interactions like support removal causing falls require conceptualizing scene evolution without the object.', 'The approach cannot rely on spatial hole filling alone and must reason about counterfactual scenarios.', 'Runway uses text prompts describing both object removal and expected scene evolution', 'Object-Wiper and DynaEdit excluded due to unavailable code', 'OmnimatteZero excluded due to acknowledged code issues'], 'section_ids': ['sec_4', 'sec_16']}
论点验证
已证实 (85%)
we propose an extension of video object removal to more dynamic scenarios. These require not only removing a specified object, but also modeling how its removal affects other objects in the scene.
The paper clearly defines the problem extension in p_3 and p_12-13, distinguishing it from prior work that only handles photometric effects. The framework Void is presented as a solution, and extensive evaluations demonstrate the capability. However,
The paper clearly defines the problem extension in p_3 and p_12-13, distinguishing it from prior work that only handles photometric effects. The framework Void is presented as a solution, and extensive evaluations demonstrate the capability. However,
已证实 (90%)
we present Void, a framework to elicit this highlevel causal reasoning from a video diffusion model.
Void is fully specified across sections 3.1-3.6 with detailed architecture (p_20), training procedure (p_17-27), and inference pipeline (p_28-30). The framework components are clearly described and evaluated. This is a concrete contribution with comp
Void is fully specified across sections 3.1-3.6 with detailed architecture (p_20), training procedure (p_17-27), and inference pipeline (p_28-30). The framework components are clearly described and evaluated. This is a concrete contribution with comp
已证实 (90%)
Void is built on advances along three axes: data construction, training strategy, and inference optimization.
All three axes are clearly described with substantial detail: data construction (p_14-16 with specific dataset generation procedures), training strategy (p_17-27 with quadmask and two-pass approach), and inference optimization (p_28-30 with VLM-guide
All three axes are clearly described with substantial detail: data construction (p_14-16 with specific dataset generation procedures), training strategy (p_17-27 with quadmask and two-pass approach), and inference optimization (p_28-30 with VLM-guide
已证实 (85%)
we propose two improvements over prior work [19]: (i) "quadmask" conditioning that explicitly identifies regions of each frame that may change after the object is removed, and (ii) a video appearance refiner applied in a second pass to remove artifacts like unwanted object morphing.
Both improvements are described in detail and evaluated. Quadmask is explained in p_17-19 with Figure 2 examples, and the second-pass refiner is described in p_22-29. Ablations in Table 4 and Table 6 provide evidence of their importance. However, the
Both improvements are described in detail and evaluated. Quadmask is explained in p_17-19 with Figure 2 examples, and the second-pass refiner is described in p_22-29. Ablations in Table 4 and Table 6 provide evidence of their importance. However, the
已证实 (90%)
We gather a new benchmark of videos with diverse and complex interactions, comprising synthetic and realworld data.
The benchmark is clearly described in p_31 with specific numbers: 75 real-world videos and 30 synthetic test videos. The diversity of interactions is specified (object manipulation, support removal, collisions, etc.). This is a concrete, verifiable c
The benchmark is clearly described in p_31 with specific numbers: 75 real-world videos and 30 synthetic test videos. The diversity of interactions is specified (object manipulation, support removal, collisions, etc.). This is a concrete, verifiable c
已证实 (90%)
We generate new counterfactual pairs to address this gap with physics-based simulations from Kubric [10] and human motion capture data from HUMOTO [25].
The counterfactual pair generation is described in detail in p_14-16 with specific numbers (~1900 Kubric pairs, ~4500 HUMOTO pairs). The methodology for creating V and V̄ pairs is clearly explained with physics simulation and re-simulation.
The counterfactual pair generation is described in detail in p_14-16 with specific numbers (~1900 Kubric pairs, ~4500 HUMOTO pairs). The methodology for creating V and V̄ pairs is clearly explained with physics simulation and re-simulation.
已证实 (90%)
we extend the trimask to a quadmask M q with a fourth color (dark grey) that describes overlap between (i) the object to be removed and (ii) other parts of the scene that are affected.
The quadmask extension is clearly described in p_19 with the fourth color (dark grey) for overlap regions. Figure 2 provides visual examples. The motivation for the extension is well-explained with a concrete example (boy catching ball).
The quadmask extension is clearly described in p_19 with the fourth color (dark grey) for overlap regions. Figure 2 provides visual examples. The motivation for the extension is well-explained with a concrete example (boy catching ball).
已证实 (95%)
we repurpose the Kubric simulation and rendering engine [10] and the HUMOTO human motion capture dataset [25].
This is a straightforward design choice claim that is clearly stated in p_4 and p_14. The use of Kubric and HUMOTO is factual and verifiable.
This is a straightforward design choice claim that is clearly stated in p_4 and p_14. The use of Kubric and HUMOTO is factual and verifiable.
已证实 (90%)
During inference, we generate quadmasks with vision-language models (VLMs), leveraging their world knowledge to expand a simple object mask into richer pixel-space guidance.
The VLM-based quadmask generation is described in detail in p_30, including the complete pipeline: VLM identifies affected objects, SAM3 generates masks, VLM predicts counterfactual positions, and quadmask is computed. This is a fully specified desig
The VLM-based quadmask generation is described in detail in p_30, including the complete pipeline: VLM identifies affected objects, SAM3 generates masks, VLM predicts counterfactual positions, and quadmask is computed. This is a fully specified desig
已证实 (95%)
We generate ∼1900 videos pairs in this manner.
Specific quantitative claim about dataset size. Stated in p_15. This is directly verifiable.
Specific quantitative claim about dataset size. Stated in p_15. This is directly verifiable.
已证实 (95%)
We randomize the textures of the objects in the scene, the background wall and the human, and generate ∼4500 video pairs.
Specific quantitative claim about dataset size and randomization procedure. Stated in p_16. This is directly verifiable.
Specific quantitative claim about dataset size and randomization procedure. Stated in p_16. This is directly verifiable.
已证实 (95%)
We propose Void, a model built upon the CogVideoX diffusion transformer backbone [40] and initialized from the weights released with Generative Omnimatte [19].
Clear design choice stated in p_20. The model architecture and initialization are factual claims about implementation.
Clear design choice stated in p_20. The model architecture and initialization are factual claims about implementation.
已证实 (90%)
We finetune it with quadmask conditioning on the counterfactual video pairs described previously.
Training procedure is described in p_20. The finetuning with quadmask conditioning on counterfactual pairs is clearly stated.
Training procedure is described in p_20. The finetuning with quadmask conditioning on counterfactual pairs is clearly stated.
已证实 (90%)
This second pass is not always required so we trigger it only when object removal is predicted to cause substantial dynamic reconfiguration.
The conditional triggering of the second pass is described in p_28. The criterion (substantial dynamic reconfiguration) is explained.
The conditional triggering of the second pass is described in p_28. The criterion (substantial dynamic reconfiguration) is explained.
已证实 (90%)
The same VLM used to create a quadmask additionally classifies whether removal induces significant object motion (e.g., free-fall or trajectory change).
The VLM classification for triggering the second pass is described in p_28. The examples (free-fall, trajectory change) are provided.
The VLM classification for triggering the second pass is described in p_28. The examples (free-fall, trajectory change) are provided.
已证实 (95%)
We test on two datasets. The first comprises 75 real-world videos involving object manipulation, support removal, collisions, articulated interactions, and shadow/reflection removal. The second is synthetic and consists of 30 Kubric and HUMOTO test videos combined with existing synthetic object removal datasets.
Specific quantitative claim about test datasets. Stated in p_31 with exact numbers (75 real-world, 30 synthetic). This is directly verifiable.
Specific quantitative claim about test datasets. Stated in p_31 with exact numbers (75 real-world, 30 synthetic). This is directly verifiable.
已证实 (95%)
All results in the main paper use Gemini 3 Pro as the VLM in this pipeline; we also report scores with GPT-5.2 and Qwen-3.5 VL in the appendix.
Clear statement about VLM choice in p_32. Factual claim about experimental setup.
Clear statement about VLM choice in p_32. Factual claim about experimental setup.
已证实 (90%)
For fair comparison, each baseline is evaluated using its preferred conditioning format: binary masks for ProPainter, DiffuEraser, ROSE, and MiniMax- Remover; trimasks for Generative Omnimatte; and natural-language editing prompts for Runway (Aleph), a commercial video editing system.
The evaluation protocol for baselines is described in p_33-34. Each baseline's conditioning format is specified. This is a methodological design choice that is clearly documented.
The evaluation protocol for baselines is described in p_33-34. Each baseline's conditioning format is specified. This is a methodological design choice that is clearly documented.
已证实 (95%)
We conduct a user study with 25 participants to measure perceptual realism and physical plausibility of counterfactual edits.
Specific quantitative claim about user study. Stated in p_36. Directly verifiable.
Specific quantitative claim about user study. Stated in p_36. Directly verifiable.
已证实 (95%)
For each participant, we randomly sample 5 out of the 75 real-world scenarios, resulting in 125 total comparisons.
Specific quantitative claim about sampling procedure. Stated in p_37 with calculation (25 participants × 5 scenarios = 125 comparisons). Directly verifiable.
Specific quantitative claim about sampling procedure. Stated in p_37 with calculation (25 participants × 5 scenarios = 125 comparisons). Directly verifiable.
... 共 40 个论点
可复现性评估
较低可复现性 (0%)
缺失的复现细节
- No code available for the proposed VOID method
- No dataset or data available - unclear what videos were used for evaluation
- Model architecture details not provided in the accessible sections
- Training hyperparameters missing (learning rate, batch size, epochs, optimizer)
- No information about loss functions or training objectives
- Hardware specifications and training time not reported
- Random seeds not specified for reproducibility
- Preprocessing details for quadmask conversion referenced in section 3.6 but not fully accessible
- Evaluation metrics implementation details not provided
- Dataset characteristics and size not specified
局限性（作者自述）
- We did not compare with Object-Wiper [17] and DynaEdit [16] as code is unavailable, nor OmnimatteZero [34] due to an acknowledged issue with their released code at the time of writing.
- Video diffusion models, especially relatively lightweight models like the 5 billion parameter CogVideoX model we build on, struggle to maintain temporal coherence when generating complex, motion-heavy videos
- Standard perceptual similarity metrics such as LPIPS, DreamSim, and feature-based similarity measures (e.g., DINOv2) are widely used for evaluating visual fidelity and perceptual similarity. While these metrics are valuable indicators of image or video similarity, they may fail to capture certain task-specific artifacts relevant to video inpainting, particularly in dynamic settings involving object interactions and causal effects.
- In some cases, methods that produce visually implausible or blurry results can achieve better scores than models with objectively more realistic inpainting outcomes.
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分析时间：2026-04-25T07:28:14+00:00 · 数据来源：Paper Collector