VOID: Video Object and Interaction Deletion
Existing video object removal methods excel at inpainting content "behind" the object and correcting appearance-level artifacts such as shadows and reflections. However, when the removed object has more significant interactions, such as collisions with other objects, current models fail to correct them and produce implausible results. We present VOID, a video object removal framework designed to perform physically-plausible inpainting in these complex scenarios. To train the model, we generate a new paired dataset of counterfactual object removals using Kubric and HUMOTO, where removing an object requires altering downstream physical interactions. During inference, a vision-language model identifies regions of the scene affected by the removed object. These regions are then used to guide a video diffusion model that generates physically consistent counterfactual outcomes. Experiments on both synthetic and real data show that our approach better preserves consistent scene dynamics after object removal compared to prior video object removal methods. We hope this framework sheds light on how to make video editing models better simulators of the world through high-level causal reasoning.
69cf64dde02fdd68b91b7b58
Saman Motamed
claimed_verified
2026-04-03T09:07:16.138Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6475c37b04c82116f9bb2356/nfBllBSa0Vr_QvUEC84t3.jpeg
Sam Motamed
sam-motamed
user
sam-motamed
69cf64dde02fdd68b91b7b59
William Harvey
69cf64dde02fdd68b91b7b5a
Benjamin Klein
69cf64dde02fdd68b91b7b5b
Luc Van Gool
69cf64dde02fdd68b91b7b5c
Zhuoning Yuan
69cf64dde02fdd68b91b7b5d
Ta-Ying Cheng
admin_assigned
2026-04-07T08:28:39.057Z
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/avatars/516769c6c3bc7a0d416924181c6866dc.svg
Ta-Ying Cheng
chengtim
user
chengtim
VOID: Video Object and Interaction Deletion
Existing video object removal methods excel at inpainting content "behind" the object and correcting appearance-level artifacts such as shadows and reflections. However, when the removed object has more significant interactions, such as collisions with other objects, current models fail to correct them and produce implausible results. We present VOID, a video object removal framework designed to perform physically-plausible inpainting in these complex scenarios. To train the model, we generate a new paired dataset of counterfactual object removals using Kubric and HUMOTO, where removing an object requires altering downstream physical interactions. During inference, a vision-language model identifies regions of the scene affected by the removed object. These regions are then used to guide a video diffusion model that generates physically consistent counterfactual outcomes. Experiments on both synthetic and real data show that our approach better preserves consistent scene dynamics after object removal compared to prior video object removal methods. We hope this framework sheds light on how to make video editing models better simulators of the world through high-level causal reasoning.
VOID is a video object removal framework that uses vision-language models and video diffusion models to generate physically plausible scenes by leveraging causal reasoning and counterfactual reasoning.
video object removal video diffusion model vision-language model counterfactual object removal physically-plausible inpainting causal reasoning video editing models