  Joint relation modeling is a curial component in human motion prediction.
Most existing methods tend to design skeletal-based graphs to build the
relations among joints, where local interactions between joint pairs are well
learned. However, the global coordination of all joints, which reflects human
motion's balance property, is usually weakened because it is learned from part
to whole progressively and asynchronously. Thus, the final predicted motions
are sometimes unnatural. To tackle this issue, we learn a medium, called
balance attractor (BA), from the spatiotemporal features of motion to
characterize the global motion features, which is subsequently used to build
new joint relations. Through the BA, all joints are related synchronously, and
thus the global coordination of all joints can be better learned. Based on the
BA, we propose our framework, referred to Attractor-Guided Neural Network,
mainly including Attractor-Based Joint Relation Extractor (AJRE) and
Multi-timescale Dynamics Extractor (MTDE). The AJRE mainly includes Global
Coordination Extractor (GCE) and Local Interaction Extractor (LIE). The former
presents the global coordination of all joints, and the latter encodes local
interactions between joint pairs. The MTDE is designed to extract dynamic
information from 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 in H3.6M, CMU-Mocap, and 3DPW.
