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.

通过学习motion的空时特征来构建全局motion特征的中介物称为协调吸引子(Coordination Attractor, CA)，进而用于构建新的相对关节关系。通过CA，可以同时关联所有关节，从而更好地学习所有关节的协调运动。基于此，我们进一步提出了一种新的联合关节关系建模模块，称为综合关节关系提取器(Comprehensive Joint Relation Extractor, CJRE)，以统一的方式将该协调关系与关节对之间的局部相互作用结合起来。此外，我们还提出了一种多时间尺度动力学提取器(Multi-timescale Dynamics Extractor, MTDE)，以从原始位置信息中提取丰富的动力学信息，用于有效的预测。大量实验证明，所提出的框架在H3.6M、CMU-Mocap和3DPW的短期和长期预测中均优于现有方法。

以关注动作协调为中心的更真实的人体动作预测