Multiscale Graph Redefining: Correlation-Based Multiscale Graph Clustering Network for Human Motion Prediction.

Abstract

Graph Convolutional Networks (GCNs) have exhibited considerable promise in 3-D skeleton-based human motion prediction. Based on the intuitive observation that human motion can be delineated through the physical interconnections among human joints, many previous works have designed multiscale graphs to learn the relationships and constraints between different graph scales, obtaining encouraging results for human motion prediction. However, these fixed multiscale graphs obtain new scale graphs by merging adjacent human joint information, ignoring implicit semantic information during dynamic movements. Furthermore, human joint correlations tend to vary randomly as the depth of the multiscale clustering graph increases, which contradicts the design concept of fixed multiscale graphs. To address these limitations, we explore a novel correlation-based multiscale graph clustering network (CMGC) for adaptive multiscale graph representation learning. Given a human joints graph, the goal of CMGC is first to generate more new graphs representing motion correlations adaptively at different scale levels and then selectively restore the derived graph scales to the original human joints graphs, which enables various motion features extraction. Moreover, we introduce the discrete wavelet transform (DWT) to compensate for the signal loss caused by discrete cosine transform (DCT) domain modeling from human motion. The CMGC gives rise to gratifying performances with the adaptive multiscale graph. Extensive experiments reveal that CMGC outperforms state-of-the-art methods by 11.2%, 10.1%, and 11.2% of 3-D mean per joint position error (MPJPE) on average on Human 3.6M, CMU Mocap, and 3DPW datasets, respectively. We also test the mean angle error (MAE) on Human3.6M, which is lower by 6.5% than previous methods. Our code is released at https://github.com/JunyuShi02/CMGC.