Dancing to Music
This addresses the challenge of music-to-dance generation for applications in entertainment or robotics, but it appears incremental as it builds on existing synthesis-by-analysis concepts.
The paper tackles the problem of generating dance movements from music by proposing a synthesis-by-analysis learning framework, which decomposes dance into basic units and composes them according to music, resulting in realistic, diverse, style-consistent, and beat-matching dances as demonstrated experimentally.
Dancing to music is an instinctive move by humans. Learning to model the music-to-dance generation process is, however, a challenging problem. It requires significant efforts to measure the correlation between music and dance as one needs to simultaneously consider multiple aspects, such as style and beat of both music and dance. Additionally, dance is inherently multimodal and various following movements of a pose at any moment are equally likely. In this paper, we propose a synthesis-by-analysis learning framework to generate dance from music. In the analysis phase, we decompose a dance into a series of basic dance units, through which the model learns how to move. In the synthesis phase, the model learns how to compose a dance by organizing multiple basic dancing movements seamlessly according to the input music. Experimental qualitative and quantitative results demonstrate that the proposed method can synthesize realistic, diverse,style-consistent, and beat-matching dances from music.