  We present a learning-based approach with pose perceptual loss for automatic
music video generation. Our method can produce a realistic dance video that
conforms to the beats and rhymes of almost any given music. To achieve this, we
firstly generate a human skeleton sequence from music and then apply the
learned pose-to-appearance mapping to generate the final video. In the stage of
generating skeleton sequences, we utilize two discriminators to capture
different aspects of the sequence and propose a novel pose perceptual loss to
produce natural dances. Besides, we also provide a new cross-modal evaluation
to evaluate the dance quality, which is able to estimate the similarity between
two modalities of music and dance. Finally, a user study is conducted to
demonstrate that dance video synthesized by the presented approach produces
surprisingly realistic results. The results are shown in the supplementary
video at https://youtu.be/0rMuFMZa_K4
