[2603.27314] TokenDance: Token-to-Token Music-to-Dance Generation with Bidirectional Mamba

Computer Science > Artificial Intelligence arXiv:2603.27314 (cs) [Submitted on 28 Mar 2026] Title:TokenDance: Token-to-Token Music-to-Dance Generation with Bidirectional Mamba Authors:Ziyue Yang, Kaixing Yang, Xulong Tang View a PDF of the paper titled TokenDance: Token-to-Token Music-to-Dance Generation with Bidirectional Mamba, by Ziyue Yang and 2 other authors View PDF HTML (experimental) Abstract:Music-to-dance generation has broad applications in virtual reality, dance education, and digital character animation. However, the limited coverage of existing 3D dance datasets confines current models to a narrow subset of music styles and choreographic patterns, resulting in poor generalization to real-world music. Consequently, generated dances often become overly simplistic and repetitive, substantially degrading expressiveness and realism. To tackle this problem, we present TokenDance, a two-stage music-to-dance generation framework that explicitly addresses this limitation through dual-modality tokenization and efficient token-level generation. In the first stage, we discretize both dance and music using Finite Scalar Quantization, where dance motions are factorized into upper and lower-body components with kinematic-dynamic constraints, and music is decomposed into semantic and acoustic features with dedicated codebooks to capture choreography-specific structures. In the second stage, we introduce a Local-Global-Local token-to-token generator built on a Bidirectional M...