arXiv AI By Jing Xu, Cunjian Chen, Qiuhong Ke

ChoreoStudio: Planning What, When, and Who in Music-Driven Group Choreography

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ChoreoStudio is a music‑driven group choreography system that explicitly plans what formations dancers should adopt, when to transition between them, and which dancers execute each transition. It uses a music disentangler to extract rhythmic and structural features, a frozen omni‑modal model to generate a global semantic script, and a Director module that outputs centroid‑relative formation points, transition gates, and mobility allocations. These high‑level plans guide a diffusion generator that produces beat‑aligned body motion, resulting in state‑of‑the‑art motion quality, group organization, and formation dynamics on the AIOZ‑GDance benchmark.

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arXiv AI
Jul 16

Music-to-Dance Generation via Atomic Movements

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
Hugging Face Trending Papers
Jun 29

OmniDance: Multimodal Driven Dance Video Generation with Large-scale Internet Data

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.

arXiv Computer Vision
Oct 9

Breaking the Group Size Barrier: Parameter-Efficient Group Dance Generation with Chain-of-Dancers

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
arXiv Computer Vision
1d ago

FFMT: Frequency-Factorized Multi-Rate Tokenization for Music-to-Dance Generation

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