  Close your eyes and listen to music, one can easily imagine an actor dancing
rhythmically along with the music. These dance movements are usually made up of
dance movements you have seen before. In this paper, we propose to reproduce
such an inherent capability of the human-being within a computer vision system.
The proposed system consists of three modules. To explore the relationship
between music and dance movements, we propose a cross-modal alignment module
that focuses on dancing video clips, accompanied on pre-designed music, to
learn a system that can judge the consistency between the visual features of
pose sequences and the acoustic features of music. The learned model is then
used in the imagination module to select a pose sequence for the given music.
Such pose sequence selected from the music, however, is usually discontinuous.
To solve this problem, in the spatial-temporal alignment module we develop a
spatial alignment algorithm based on the tendency and periodicity of dance
movements to predict dance movements between discontinuous fragments. In
addition, the selected pose sequence is often misaligned with the music beat.
To solve this problem, we further develop a temporal alignment algorithm to
align the rhythm of music and dance. Finally, the processed pose sequence is
used to synthesize realistic dancing videos in the imagination module. The
generated dancing videos match the content and rhythm of the music.
Experimental results and subjective evaluations show that the proposed approach
can perform the function of generating promising dancing videos by inputting
music.
