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
InterSing is a framework that generates realistic 3D head animations for duet singing by modeling the sparse, rhythm‑dependent interactions between performers. It introduces interaction logits—a weakly supervised, interpretable latent representation of cross‑performer engagement—and uses them to condition an interaction‑aware diffusion model driven by audio and interaction dynamics. The approach enables unified multi‑mode generation, producing coordinated behavior, independent motion, and smooth transitions, and it generalizes to multi‑singer performances with intuitive control over engagement.
By Yihan Zhou, Zikai Huang, Yuyang Yu, Xuemiao Xu, Cheng Xu, Shengfeng He
arXiv:2507. 19684v2 Announce Type: replace-cross Abstract: Socially interactive humanoid robots must engage with humans through their bodies, adapting in real time to a partner's movement, intent, and abilities.
By Bermet Burkanova, Yasaman Etesam, Payam Jome Yazdian, Trinity Evans, Chuxuan Zhang, Zoe Stanley, Paige Tutt\"os\'i, Angelica Lim
In recent years, advancements in deep learning and generative models have revolutionized music-driven dance generation. This paper introduces a novel platform, namely DanceDuo, leveraging diffusion models to generate AI-choreographed dance sequences synchronized with a variety of music genres, to encourage dancing practice.
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: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