arXiv Computer Vision
Motion Concept Unlearning in Video Diffusion Models
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Why Do Video Diffusion Models Violate Physics? Unveiling the Flaws in Attention Mechanisms
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CleanVideo: Adaptive Concept Erasure for Text-to-Video Diffusion Models
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Inference-Time Concept Suppression and Video-Centric Evaluation for Text-to-Video Models
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Sep 3
EraseSAE: Surgical Concept Erasure in Text-to-Video Diffusion Models via Sparse Autoencoders
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EraseSAE: Surgical Concept Erasure in Text-to-Video Diffusion Models via Sparse Autoencoders
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FOMO: Forget the Concept, Don't Miss Out on the Scene in Selective Video Unlearning
FOMO is a training‑based selective video unlearning method that prioritizes preserving the original scene while removing targeted concepts. It localizes concept‑related representations for modification and employs a preservation mechanism that maintains non‑target scene information without auxiliary data. The approach extends to motion unlearning, enabling removal of concepts defined by temporal behavior, and achieves a strong balance between concept removal and scene preservation.