  3D human pose estimation is a vital task in computer vision, involving the
prediction of human joint positions from images or videos to reconstruct a
skeleton of a human in three-dimensional space. This technology is pivotal in
various fields, including animation, security, human-computer interaction, and
automotive safety, where it promotes both technological progress and enhanced
human well-being. The advent of deep learning significantly advances the
performance of 3D pose estimation by incorporating temporal information for
predicting the spatial positions of human joints. However, traditional methods
often fall short as they primarily focus on the spatial coordinates of joints
and overlook the orientation and rotation of the connecting bones, which are
crucial for a comprehensive understanding of human pose in 3D space. To address
these limitations, we introduce Quater-GCN (Q-GCN), a directed graph
convolutional network tailored to enhance pose estimation by orientation. Q-GCN
excels by not only capturing the spatial dependencies among node joints through
their coordinates but also integrating the dynamic context of bone rotations in
2D space. This approach enables a more sophisticated representation of human
poses by also regressing the orientation of each bone in 3D space, moving
beyond mere coordinate prediction. Furthermore, we complement our model with a
semi-supervised training strategy that leverages unlabeled data, addressing the
challenge of limited orientation ground truth data. Through comprehensive
evaluations, Q-GCN has demonstrated outstanding performance against current
state-of-the-art methods.
