Towards More Realistic Human Motion Prediction With Attention to Motion Coordination

Abstract

Joint relation modeling is a curial component in human motion prediction.\nMost existing methods rely on skeletal-based graphs to build the joint\nrelations, where local interactive relations between joint pairs are well\nlearned. However, the motion coordination, a global joint relation reflecting\nthe simultaneous cooperation of all joints, is usually weakened because it is\nlearned from part to whole progressively and asynchronously. Thus, the final\npredicted motions usually appear unrealistic. To tackle this issue, we learn a\nmedium, called coordination attractor (CA), from the spatiotemporal features of\nmotion to characterize the global motion features, which is subsequently used\nto build new relative joint relations. Through the CA, all joints are related\nsimultaneously, and thus the motion coordination of all joints can be better\nlearned. Based on this, we further propose a novel joint relation modeling\nmodule, Comprehensive Joint Relation Extractor (CJRE), to combine this motion\ncoordination with the local interactions between joint pairs in a unified\nmanner. Additionally, we also present a Multi-timescale Dynamics Extractor\n(MTDE) to extract enriched dynamics from the raw position information for\neffective prediction. Extensive experiments show that the proposed framework\noutperforms state-of-the-art methods in both short- and long-term predictions\non H3.6M, CMU-Mocap, and 3DPW.\n