Music2Dance: DanceNet for Music-driven Dance Generation
This addresses the challenge of music-driven dance generation for applications in entertainment and animation, though it appears incremental with a focus on improving realism and diversity.
The paper tackles the problem of generating realistic and diverse 3D dance motions from music by proposing DanceNet, an autoregressive generative model that uses music style, rhythm, and melody as control signals, achieving state-of-the-art results.
Synthesize human motions from music, i.e., music to dance, is appealing and attracts lots of research interests in recent years. It is challenging due to not only the requirement of realistic and complex human motions for dance, but more importantly, the synthesized motions should be consistent with the style, rhythm and melody of the music. In this paper, we propose a novel autoregressive generative model, DanceNet, to take the style, rhythm and melody of music as the control signals to generate 3D dance motions with high realism and diversity. To boost the performance of our proposed model, we capture several synchronized music-dance pairs by professional dancers, and build a high-quality music-dance pair dataset. Experiments have demonstrated that the proposed method can achieve the state-of-the-art results.