Multi-Channel Spatio-Temporal GCN for Human Pose Forecasting

Human pose forecasting predicts future human postures based on a given sequence of human postures. The fusion of spatio-temporal joint dependencies has a significant impact on model output, but research in this area is relatively scarce. This paper introduces a spatiotemporal graph convolutional neural network that effectively fuses multi-channel information with learnable multi-channel topology. We have designed a multi-branch module to capture temporal features from diverse spatial domains. Moreover, we initiatively adopted DST to optimize long-term forecasting. Our network has been evaluated on 3DPW and AMASS datasets commonly used in motion prediction. The results of the experiments showed that our methods were up to the current state of the art.