Multi-Scale Cascaded Generator for Music-driven Dance Synthesis

This is the authors' abstract. We don't add key points for this paper.

Dance is a creative performance art and must keep coherent with the rhythm and style of music. To address these issues, most of the existing music-driven dance synthesis methods utilize deep generative models and capture the dynamic characteristics of dance motions. However, we observe that dance motions contain big-scale body part movements and small-scale joint movements that are mutually coordinated and related, and the generated dance motions, particularly affected by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L₁</tex> loss, are too restrictive and conservative. In this paper, we propose a multi-scale cascaded music-driven dance synthesis network (MC-MDSN) that first generates big-scale body motions conditioned on music and then further refines local small-scale joint motions. Furthermore, we design a multi-scale feature loss to capture the dynamic characteristics of each scale motions and the relations between different scale motion joints. Experimental results show that our method generates better dance motions than the baselines.