  Dance and music are two highly correlated artistic forms. Synthesizing dance
motions has attracted much attention recently. Most previous works conduct
music-to-dance synthesis via directly music to human skeleton keypoints
mapping. Meanwhile, human choreographers design dance motions from music in a
two-stage manner: they firstly devise multiple choreographic dance units
(CAUs), each with a series of dance motions, and then arrange the CAU sequence
according to the rhythm, melody and emotion of the music. Inspired by these, we
systematically study such two-stage choreography approach and construct a
dataset to incorporate such choreography knowledge. Based on the constructed
dataset, we design a two-stage music-to-dance synthesis framework ChoreoNet to
imitate human choreography procedure. Our framework firstly devises a CAU
prediction model to learn the mapping relationship between music and CAU
sequences. Afterwards, we devise a spatial-temporal inpainting model to convert
the CAU sequence into continuous dance motions. Experimental results
demonstrate that the proposed ChoreoNet outperforms baseline methods (0.622 in
terms of CAU BLEU score and 1.59 in terms of user study score).
