Netflix's VOID shows video editing has finally learned the laws of physics
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Netflix and INSAIT researchers introduced VOID (Video Object and Interaction Deletion), a system that reframes video object removal as causal simulation rather than pixel inpainting. Instead of filling masked pixels, VOID uses a Vision-Language Model to identify 'causal ripples' an object leaves — shadows, reflections, physical interactions — and generates a 'quadmask' to guide a modified CogVideoX diffusion model. A two-pass pipeline addresses the 'jelly problem' where simulated moving objects deform, using flow-warped noise to maintain rigidity. Training relied on synthetic 3D data from Kubric and HUMOTO. Netflix has open-sourced the model, though it requires 40GB+ VRAM. Community reaction includes excitement about physics-aware editing and concerns about media manipulation and censorship.
