  Graph convolutional networks have significantly improved 3D human pose
estimation by representing the human skeleton as an undirected graph. However,
this representation fails to reflect the articulated characteristic of human
skeletons as the hierarchical orders among the joints are not explicitly
presented. In this paper, we propose to represent the human skeleton as a
directed graph with the joints as nodes and bones as edges that are directed
from parent joints to child joints. By so doing, the directions of edges can
explicitly reflect the hierarchical relationships among the nodes. Based on
this representation, we further propose a spatial-temporal conditional directed
graph convolution to leverage varying non-local dependence for different poses
by conditioning the graph topology on input poses. Altogether, we form a
U-shaped network, named U-shaped Conditional Directed Graph Convolutional
Network, for 3D human pose estimation from monocular videos. To evaluate the
effectiveness of our method, we conducted extensive experiments on two
challenging large-scale benchmarks: Human3.6M and MPI-INF-3DHP. Both
quantitative and qualitative results show that our method achieves top
performance. Also, ablation studies show that directed graphs can better
exploit the hierarchy of articulated human skeletons than undirected graphs,
and the conditional connections can yield adaptive graph topologies for
different poses.
