Spatial-Temporal Correlation Modeling for Motion Prediction
Yingying Jiao, Haipeng Chen, Chang Yao, Pengxiang Su, Chong Fu, Xiang Wang · 2022 IEEE International Conference on Multimedia and Expo (ICME) · 2022
Human motion prediction is fundamental for many applications in computer vision. Current methods typically handle motion prediction with seqential models, which ignore the fact that joint movement is driven by forces. In this paper, we provide a novel mechanical view to decompose force into magnitude and direction, which contributes to modeling the temporal evolution of joints. Moreover, existing graph convolution-based methods merely utilize the deep-level features, which is difficult to capture the complex spatial dependencies contexts. We introduce a novel spatial connections encoding model to capture the multi-level spatial dependencies between joints. Finally, to encode abundant temporal dependencies, we present a multi-head temporal encoding module. Comprehensive experiments show that our model sets the state-of-the-art performance on the largest human motion benchmark datasets.