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: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
arXiv:2502. 00023v2 Announce Type: replace-cross Abstract: Our research explores the development and application of musical agents, human-in-the-loop generative AI systems designed to support music performance and improvisation within co-creative spaces.
By Keon Ju M. Lee, Philippe Pasquier
arXiv:2606. 19727v1 Announce Type: cross Abstract: Language models have become essential tools in shaping modern workflows.
By Punit Kumar Singh, Niladri Ghosh, Advait Joshi{\i}nst, Shailee Choudhary, Michael F\"arber, Haiqin Yang
Recent advances in AI music generation have enabled users to create complete musical pieces from natural-language prompts. However, most existing systems follow a prompt-and-regenerate paradigm, making iterative refinement difficult because users must repeatedly recreate compositions instead of directly evolving existing musical ideas.
arXiv:2607. 24873v1 Announce Type: new Abstract: Recent advances in AI music generation have enabled users to create complete musical pieces from natural-language prompts.
By Callie C. Liao, Duoduo Liao, Ellie L. Zhang
arXiv:2606. 24307v1 Announce Type: cross Abstract: Interactive music and live performance relies on real-time human expression, but modern generative music AI remains largely absent from this domain due to its prohibitive inference latency and offline rendering paradigm.
By Baisen Wang, Chenxi Bao, Qisong Han
As generative platforms such as Suno and Udio reach human-grade audio quality, the scope of AI's utility has expanded across the entire music production workflow. Beyond simple track generation, these advancements have catalyzed the adoption of AI-driven methodologies in diverse forms.
arXiv:2512. 02652v2 Announce Type: replace-cross Abstract: Existing methods for expressive music performance rendering, a conditional generation task that aims to generate a human-like performance from a symbolic score, rely on supervised learning over small labeled datasets, which limits scaling of both data volume and model size, despite the availability of vast unlabeled music, as in vision and language.
By Hong-Jie You, Jie-Jing Shao, Xiao-Wen Yang, Lin-Han Jia, Lan-Zhe Guo, Yu-Feng Li
arXiv:2608. 16222v1 Announce Type: cross Abstract: Humanoid intelligence requires learning over an extremely diverse space of whole-body motions and physically grounded interactions.
By Jiahao Ji, Ji Ma, Runhan Zhang, Runyi Yu, Wenjia Wang, Weiheng Chi, Qianqian Peng, Weichao Yan, Yongfei Gu, Ye Tian, Ting Wu, Longwei Li, Chun Yuan, Ruoli Dai, Lei Han
arXiv:2606. 01686v1 Announce Type: cross Abstract: As generative platforms such as Suno and Udio reach human-grade audio quality, the scope of AI's utility has expanded across the entire music production workflow.
By Seonghyeon Go, Yumin Kim