TeMuDance: Contrastive Alignment-Based Textual Control for Music-Driven Dance Generation
Existing music-driven dance generation approaches have achieved strong realism and effective audio-motion alignment. However, they generally lack semantic controllability, making it difficult to guide specific movements through natural language descriptions. This limitation primarily stems from the absence of large-scale datasets that jointly align music, text, and motion for supervised learning of text-conditioned control. To address this challenge, we propose TeMuDance, a framework that enables text-based control for music-conditioned dance generation without requiring any manually annotated music-text-motion triplet dataset. TeMuDance introduces a motion-centred bridging paradigm that ...
| Authors | Xinran Liu, Diptesh Kanojia, Wenwu Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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