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Existing kinematic skeleton-based 3D human pose estimation methods onlypredict joint positions. Although this is sufficient to compute the yaw andpitch of the bone rotations, the roll around the axis of the bones remainsunresolved by these methods. In this paper, we propose a novel 2D-to-3D liftingGraph Convolution Network named PoseGraphNet++ to predict the complete humanpose including the joint positions and the bone orientations. We employ nodeand edge convolutions to utilize the joint and bone features. Our model isevaluated on multiple benchmark datasets, and its performance is either on parwith or better than the state-of-the-art in terms of both position and rotationmetrics. Through extensive ablation studies, we show that PoseGraphNet++benefits from exploiting the mutual relationship between the joints and thebones.

PoseGraphNet++: Enriching 3D Human Pose with Orientation Estimation