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HuggingFace Daily Papers(社区热门论文)· HuggingFace Daily Papers(社区热门论文)·· 8 天前AI 评分42

VideoPhysEdit:基于刚体物理场景重建的物理反事实视频编辑

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VideoPhysEdit 提出免训练的物理反事实视频编辑(PCVE)流程,通过刚体物理场景重建还原可复现原视频运动与交互的仿真场景,从而把物理编辑作为干预并生成反事实视频。

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Abstract:Video editing has advanced substantially in recent years, with methods increasingly accounting for the visual consequences of edits, such as changes to shadows and occlusions. However, the physical consequences of edits, including changes to subsequent motion and interactions, remain less explored. We formulate this problem as physical counterfactual video editing (PCVE), which aims to generate a counterfactual video depicting the resulting motion and interactions given a source video, a physical edit, and its execution frame. PCVE is challenging because it requires understanding scene physics and inferring the downstream motion and interactions induced by a physical intervention, while paired factual and counterfactual data and dedicated evaluation metrics are lacking. We introduce VideoPhysEdit, a new training-free pipeline for PCVE in rigid-body scenes. It makes physical reasoning explicit through a novel physical scene reconstruction method that recovers a scene reproducing the observed motion and interactions under simulation, enabling the pipeline to apply physical edits as interventions and use the resulting trajectories to guide counterfactual video generation. We further construct PCVE-RigidBench, a synthetic benchmark with paired source and counterfactual target videos and physical ground truth, and introduce the Physical Edit Score. VideoPhysEdit achieves substantially higher physical edit accuracy than open-source methods and commercial models while maintaining competitive visual fidelity. Its Physical Edit Score is 0.376, the only positive score among the compared methods. Qualitative comparisons on real videos further show that VideoPhysEdit applies to real-world scenes and better depicts the downstream motion and interactions induced by the edits than the compared methods. Code: this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.35134 [cs.CV]
  (or arXiv:2609.35134v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2609.35134

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Conghan Yue [view email]
[v1] Mon, 28 Sep 2026 13:25:37 UTC (45,076 KB)

来源:HuggingFace Daily Papers(社区热门论文) · arxiv.org