Adaptive Spatial-Temporal Modelling for Human Motion Prediction

Human motion prediction is the process of predicting future motion sequences based on past motion sequences. The graph convolution methods currently used for modelling human motion are effective in capturing the interrelationships between joints. However, these works lack skeleton constraints on the learning of graph filters, and DCT-based temporal modeling methods produce overly smooth motion representations and ignore the learning of human motion details. In this paper, we propose a network that uses adaptive spatial graph convolution and temporal self-attention to improve human motion prediction. The adaptive graph convolution effectively enhances cross-scale spatial interaction of joint movements based on different motion patterns. Meanwhile, temporal self-attention, combined with historical motion attention, improves the learning of motion temporal information. Our proposed network achieved state-of-the-art performance on two benchmark datasets, as demonstrated by extensive experiments.