  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.
