VOID: Video Object and Interaction Deletion
Saman Motamed, William Harvey, Benjamin Klein, Luc Van Gool, Zhuoning Yuan, Ta-Ying Cheng

TL;DR
VOID is a novel video object removal framework that achieves physically plausible inpainting in complex scenarios involving object interactions by combining scene understanding and diffusion models.
Contribution
The paper introduces a new dataset and a method that integrates vision-language models with video diffusion to improve scene consistency after object removal.
Findings
Outperforms prior methods in preserving scene dynamics.
Generates more physically consistent inpainted videos.
Effective on both synthetic and real data.
Abstract
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…
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Code & Models
- 🤗netflix/void-modelmodel· ♡ 931♡ 931
- 🤗a30wattbulb/void-modelmodel
- 🤗caiovicentino1/VOID-Netflix-HLWQ-Q5model· 19 dl· ♡ 719 dl♡ 7
- 🤗Cirrascale/void-modelmodel
- 🤗veeam/void-modelmodel
- 🤗WallyDesktop/void-modelmodel
- 🤗AdversaLLC/void-modelmodel
- 🤗dlindsey/void-modelmodel
- 🤗ErenAta00/gargantuamodel
- 🤗soha23/void-modelmodel
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