MSGAT: Multi-Stage Graph Attention Network For Human Motion Prediction

Abstract

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Human motion prediction (HMP) refers to predicting the future body pose from the historical pose sequence. Many existing methods use Graph Convolutional Networks (GCN) to model the human body and convert the human pose from the pose space to the trajectory space or 3D coordinates. Furthermore, GCN treat human poses as a generic graph formed by links between each pair of body joints to encode the dependence of human spatial poses as well as temporal information by working in trajectory space. We design a multi-stage distributed processing network that includes Spatial Dense Graph Convolutional Networks (S-DGCN) and Temporal Dense Graph Convolutional Networks (T-DGCN). The multistage strategy enables us to gradually acquire smoother inputs. Additionally, we have incorporated an attention mechanism within the processing framework, which helps T-DGCN better capture temporal dependencies. As a result, the proposed network not only facilitates more effective feature extraction but also achieves state-of-the-art performance on the CMU-Mocap and 3DPW datasets. Our code is available at https://github.com/ihavenotgoodname/MSGAT.