PoseGraphNet++: Enriching 3D Human Pose with Orientation Estimation
Soubarna Banik Edvard Avagyan Alejandro Mendoza Gracia Alois Knoll
Existing kinematic skeleton-based 3D human pose estimation methods only
predict joint positions. Although this is sufficient to compute the yaw and
pitch of the bone rotations, the roll around the axis of the bones remains
unresolved by these methods. In this paper, we propose a novel 2D-to-3D lifting
Graph Convolution Network named PoseGraphNet++ to predict the complete human
pose including the joint positions and the bone orientations. We employ node
and edge convolutions to utilize the joint and bone features. Our model is
evaluated on multiple benchmark datasets, and its performance is either on par
with or better than the state-of-the-art in terms of both position and rotation
metrics. Through extensive ablation studies, we show that PoseGraphNet++
benefits from exploiting the mutual relationship between the joints and the
bones.