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In this work, we present a transformer-based learning framework for 3D dance generation conditioned on music. We carefully design our network architecture and empirically study the keys for obtaining qualitatively pleasing results. In addition, we propose a new dataset of paired 3D motion and music called AIST++, which contains 1.1M frames of 3D dance motion in 1408 sequences, covering 10 genres of dance choreographies and accompanied with multi-view camera parameters. To our knowledge it is the largest dataset of this kind.
Ruilong Li (University of Southern California)
Ph.D. student
Ruilong Li is a second year PhD student at the University of Southern California, advised by Hao Li. His research lies at the intersection field of Computer Vision and Graphics, with focus on human digitization and synthesis. His recent work on Volumetric Human Teleportation has been awarded Best In Show at SIGGRAPH Real-Time Live 2020. He interned at GoogleAI in 2020 and received his BSc degree in Physics and Mathematics, as well as his MSc degree in Computer Science, both from Tsinghua University.