SuperMotion: Source-Preserving Denoising for Text-Driven Human Motion Editing
Text-driven human motion editing aims to realize a requested change while preserving compatible source content. Existing diffusion editors rely largely on learned conditioning for preservation of the unedited part, yet their outputs can lose temporal detail as denoising proceeds. We propose the \textbf{Source-Preserving Denoising framework (SuperMotion)}, which explicitly reuses the source at each reverse step for source preservation. We first align the source motion to the output timeline and predict a preservation gate that controls reuse across frames and feature dimensions. A clean-space source anchor then utilizes the learned preservation gate to blend the predicted clean motion with...
| Authors | Fa-Ting Hong, Peter Wonka |
|---|---|
| Primary category | cs.CV |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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