arXiv — cs.AI preprintsInternational8 October 2026
Diverse Motion Customization via Control-based Dynamic Optimization
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arXiv:2610.07911v2 Announce Type: cross Abstract: Despite recent advances in video generation, motion customization remains challenging due to content leakage, where appearance attributes from the reference video unintentionally propagate into the generated output. We identify this issue as a consequence of the generative process collapsing toward the reference video, which arises from formulating the learning objective as a direct regression on the reference. To address this, we propose Control-based Motion Customization (CMC), a principled training framework that is structurally robust to co
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