  3D human pose estimation is a difficult task, due to challenges such as
occluded body parts and ambiguous poses. Graph convolutional networks encode
the structural information of the human skeleton in the form of an adjacency
matrix, which is beneficial for better pose prediction. We propose one such
graph convolutional network named PoseGraphNet for 3D human pose regression
from 2D poses. Our network uses an adaptive adjacency matrix and kernels
specific to neighbor groups. We evaluate our model on the Human3.6M dataset
which is a standard dataset for 3D pose estimation. Our model's performance is
close to the state-of-the-art, but with much fewer parameters. The model learns
interesting adjacency relations between joints that have no physical
connections, but are behaviorally similar.
