AI RESEARCH
TeMuDance: Contrastive Alignment-Based Textual Control for Music-Driven Dance Generation
arXiv CS.CV
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ArXi:2604.17005v1 Announce Type: new 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.