  Existing skeleton-based 3D human pose estimation methods only predict joint
positions. Although the yaw and pitch of bone rotations can be derived from
joint positions, the roll around the bone axis remains unresolved. We present
PoseGraphNet++ (PGN++), a novel 2D-to-3D lifting Graph Convolution Network that
predicts the complete human pose in 3D including joint positions and bone
orientations. We employ both node and edge convolutions to utilize the joint
and bone features. Our model is evaluated on multiple datasets using both
position and rotation metrics. PGN++ performs on par with the state-of-the-art
(SoA) on the Human3.6M benchmark. In generalization experiments, it achieves
the best results in position and matches the SoA in orientation, showcasing a
more balanced performance than the current SoA. PGN++ exploits the mutual
relationship of joints and bones resulting in significantly \SB{improved}
position predictions, as shown by our ablation results.
