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Fine-tuning video diffusion models with physics-supervised data and VLM-guided rewards unlocks unprecedented control over physical properties like friction and restitution, creating more realistic and controllable video generation.
Video diffusion models already contain implicit multi-view knowledge, making them surprisingly effective for novel view synthesis when adapted to ignore temporal coherence.
