Omni-Supervised Motion Editing: Balancing Change and Invariance through Positive-Negative Learning
Text-based human motion editing aims to modify existing motion sequences according to natural language instructions while maintaining the consistency of the original motion. Existing diffusion-based approaches often rely on heuristic similarity cues or coarse global conditioning, leading to motion distortion and suboptimal semantic alignment. The key challenge lies in balancing change (i.e. precisely editing target regions) and invariance (i.e. preserving unedited parts). To handle such challenge, we propose an Omni-Supervised Positive-Negative Learning framework, named OmniME. Our method integrates three complementary components: (1) retrospective feature supervision that enforces coarse...
| Authors | Zhenwu Shi, Jingyu Gong, Peiwei Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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