Recurrent Graph Convolutional Autoencoder for Unsupervised Skeleton-Based Action Recognition

Han Yao, Sev-Jue Zhao, Chi Xie, Kenan Ye, Shuang Liang

Abstract: Skeleton-based action recognition is a significant task in computer vision due to its robustness and wide application. Most unsupervised methods do not employ topological information of skeleton graphs, which actually ignore the spatial dependencies of action sequences. In this paper, we introduce a Recurrent Graph Convolutional Autoencoder (RGCA) for unsupervised action recognition from skeleton data. Our method explicitly exploits the spatial relationships among every frame’s joints while preserving the long-term temporal dynamics in whole sequences. Moreover, a Spatial Joints Attention Module is employed to measure the importance of joints in the input sequence automatically. We conduct experiments on three datasets (NTU RGB+D 60, NW-UCLA, and UWA3D) and exceed the state-of-the-art performance.