arXiv Artificial Intelligence

Diverse Motion Customization via Control-based Dynamic Optimization

Diverse Motion Customization via Control-based Dynamic Optimization

Quick summary

arXiv:2610.07911v1 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

Key takeaways

  • arXiv:2610.07911v1 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

Why it matters

The importance of “Diverse Motion Customization via Control-based Dynamic Optimization” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗