3D human pose estimation errors would propagate along the human body topology and accumulate at the end joints of limbs. Inspired by the backtracking mechanism in automatic control systems, we design an Intra-Part Constraint module that utilizes the parent nodes as the reference to build topological constraints for end joints at the part level. Further considering the hierarchy of the human topology, joint-level and body-level dependencies are captured via graph convolutional networks and self-attentions, respectively. Based on these designs, we propose a novel Human Topology aware Network (HTNet), which adopts a channel-split progressive strategy to sequentially learn the structural priors of the human topology from multiple semantic levels: joint, part, and body. Extensive experiments show that the proposed method improves the estimation accuracy by 18.7% on the end joints of limbs and achieves state-of-the-art results on Human3.6M and MPI-INF-3DHP datasets. Code is available at https://github.com/vefalun/HTNet.

翻译：三维人体姿态估计的误差会沿着人体拓扑结构传播，并累积在肢体末端关节。受自动控制系统中回溯机制的启发，我们设计了部件内约束模块，利用父节点作为参考，在部件层面对末端关节建立拓扑约束。进一步考虑人体拓扑的层次结构，分别通过图卷积网络和自注意力机制捕获关节级和身体级依赖。基于这些设计，我们提出了一种新型人体拓扑感知网络（HTNet），该网络采用通道分离的渐进式策略，从关节、部件和身体三个语义层面顺序学习人体拓扑的结构先验。大量实验表明，所提方法在肢体末端关节上使估计精度提升18.7%，并在Human3.6M和MPI-INF-3DHP数据集上达到最先进水平。代码已开源至https://github.com/vefalun/HTNet。