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
Zero-shot text-guided editing of real-world music recordings requires balancing semantic modification with faithful preservation of the original musical structure. Although recent diffusion transformers trained with rectified flow have achieved remarkable success in text-to-music generation, extending them to edit existing recordings remains challenging because editing requires accurate deterministic inversion, reliable structural preservation, and numerically stable integration throughout the inversion and generation processes.
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
arXiv:2607. 17526v1 Announce Type: cross Abstract: Zero-shot text-guided editing of real-world music recordings requires balancing semantic modification with faithful preservation of the original musical structure.
By Ali Boudaghi, Hadi Zare
arXiv:2606. 01703v1 Announce Type: cross Abstract: We address the challenge of generating high-fidelity, long-form soundtracks that remain coherent across scene transitions.
By Jiashuo Yu, Yao Yao, Boyu Chen, Alex Wang