Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation
Choreographic motion generation poses unique challenges for AI, demanding precise semantic control over complex, temporally structured, and expressive full-body dynamics. While existing models can synthesize motion from music, they remain largely black boxes. Conversely, attempting to condition generation on both text and music frequently leads to modality collapse, where dense acoustic rhythms overwhelm sparse semantic text prompts, destroying user controllability. To resolve this spatial-temporal conflict, we propose STREAM (Structural-Temporal Rhythmic Energy-based Attention for Motion), a modality-decoupled diffusion transformer. STREAM strictly separates conditioning pathways: global...
| Authors | Seong Jong Yoo, Siyuan Peng, Felix Gu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 June 2026 |
| Last updated at source | 3 July 2026 |
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