VOID: Video Object and Interaction Deletion

Existing video object removal methods excel at inpaintingcontent “behind” the object and correcting appearance-level artifactssuch as shadows and reflections. However, when the removed object hasmore significant interactions, such as collisions with other objects, cur-rent models fail to correct them and produce implausible results. Wepresent VOID, a video object removal framework designed to performphysically-plausible inpainting in these complex scenarios. To train themodel, we generate a new paired dataset of counterfactual object re-movals using Kubric and HUMOTO, where removing an object requiresaltering downstream physical interactions. During inference, a vision-language model identifies regions of the scene affected by the removedobject. These regions are then used to guide a video diffusion model thatgenerates physically consistent counterfactual outcomes. Experiments onboth synthetic and real data show that our approach better preservesconsistent scene dynamics after object removal compared to prior videoobject removal methods. We hope this framework sheds light on howto make video editing models better simulators of the world throughhigh-level causal reasoning.