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