Graph Convolutional GRU for Music-Oriented Dance Choreography Generation

Yuxin Hou, Hongxun Yao, Haoran Li

Abstract: Music-oriented dance choreography is a complex art form that requires consideration of various factors in music understanding and physical aesthetics. In addition, learning model to generate dance sequences automatically needs to overcome not only the challenges from choreography but also the complexity of human skeleton structure. In this paper, we propose a graph convolutional GRU (GraphGRU) model for music-oriented dance choreography by integrating the graph convolutional networks into the gated recurrent unit. The GraphGRU can model the spatial relationship among the human skeleton joints and the temporal relationship of the dancing sequences. Besides, we define multiple dance-driven kinematic graphs for GraphGRU and establish a path attention block to fuse the graph features. Moreover, we design a reverse-generation loss to ensure consistency between the generated dance and the music. Extensive experiments demonstrate that the proposed model GraphGRU can effectively solve the task of dance choreography.