arXiv:2607. 13978v1 Announce Type: cross Abstract: Music-driven dance generation aims to produce human motion that is both rhythmically synchronized and semantically consistent with music.
By Xinhao Cai, Yixuan Sun, Minghang Zheng, Qingchao Chen, Xin Jin, Song-chun Zhu, Yang Liu
arXiv:2606. 22726v2 Announce Type: replace Abstract: Choreographic motion generation poses unique challenges for AI, demanding precise semantic control over complex, temporally structured, and expressive full-body dynamics.
By Seong Jong Yoo, Siyuan Peng, Felix Gu, Stratis Aloimonos, Cornelia Ferm\"uller
Music-driven dance video generation aims to synthesize expressive human motion that is temporally aligned with music while maintaining high visual fidelity. Despite recent progress, existing methods still face two key limitations: the lack of large-scale, high-quality dance video datasets, and the absence of principled frameworks for integrating music as a complementary conditioning signal into Video Generation Foundation Models.
ChainDance is a scalable framework for generating coordinated group dance choreography from music, overcoming the fixed‑size limitation of prior transformer‑based methods. It reformulates the task as a Chain‑of‑Dancers, using a frozen single‑dancer diffusion backbone plus two lightweight modules—Role‑Aware Text Encoder (RATE) and Group‑Aware Motion Encoder (GAME)—to condition on per‑dancer semantics and aggregate previously generated dancers. Experiments on AIOZ‑GDance show that ChainDance delivers state‑of‑the‑art motion quality and group coordination while preserving individual identities, using 3–4× fewer parameters and 3–6× less training time than existing approaches.
By Jing Xu, Cunjian Chen, Qiuhong Ke
The paper introduces Frequency-Factorized Multi-Rate Tokenization (FFMT) for music-to-dance generation, separating root-relative motion into a low-frequency choreographic scaffold and a high-frequency residual. FFMT assigns a lower token rate and smaller codebook to the low-frequency component, while using a higher token rate and larger codebook for the high-frequency part, enabling a plan-then-articulate autoregressive factorization. Experiments on AIST++ and FineDance demonstrate strong distributional fidelity, realistic diversity, and music–motion synchronization, with ablations confirming the benefits of frequency factorization, asymmetric temporal allocation, and low-to-high conditioning.
By Qirong Guo, Ruijie Li, Zeyan Li, Sicheng Shu, Guanzhi Ding, Weihang Ding
arXiv:2606. 19914v1 Announce Type: cross Abstract: Art has long stood as a pivotal expression of human creativity.
By Xuetao Li, Wenke Huang, Mang Ye, Zijian Liu, Jinhua Xie, Jifeng Xuan, Miao Li