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

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.

3D Human Pose Regression using Graph Convolutional Network

3D human pose estimation is a difficult task, due to challenges such as occluded body parts and ambiguous poses. Graph convolutional networks encode the structural information of the human skeleton in the form of an adjacency matrix, which is beneficial for better pose prediction. We propose one such graph convolutional network named PoseGraphNet for 3D human pose regression from 2D poses. Our network uses an adaptive adjacency matrix and kernels specific to neighbor groups. We evaluate our model on the Human3.6M dataset which is a standard dataset for 3D pose estimation. Our model's performance is close to the state-of-the-art, but with much fewer parameters. The model learns interesting adjacency relations between joints that have no physical connections, but are behaviorally similar.