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.

提出了一种新的二维到三维抬升图卷积网络（PoseGraphNet++）用于预测完整的人体姿势，包括关节位置和骨骼方向。通过利用节点和边的卷积，该模型在多个基准数据集上评估，在位置和旋转指标方面的性能与现有方法相当或更好。通过大量的消融研究，证明了PoseGraphNet++从关节和骨骼之间的相互关系中获益。

PoseGraphNet++：丰富三维人体姿势与方向估计