2-D-to-3-D Lifting Human Pose Estimation Based on Simple Graph Convolution Regression

In 2-D-to-3-D human pose estimation (HPE), the torso connection relationship between joints, which can be seen as important constraint information, is critical to improving the accuracy of 3-D HPE. Previous 3-D HPE methods based on CNNs and transformers do not utilize the constraint information of nodes. GCN-based 3-D HPE methods consider joints as nodes and torsos as edges. When learning node features, the weight values of edges determine the strength of information interaction between nodes. We propose a simple graph convolution layer for regression (SGCR) for solving the node regression task. Compared with the graph convolutional network (GCN) layer, the SGCR layer has a larger receptive field, which can make full use of the weight relationship of edges. By stacking SGCR layers, we construct simple graph convolution regression layer for human pose estimation (SGCRPose) for monocular single-frame 2-D-to-3-D HPE. SGCRPose consists of an up-sampling stage, a residual SGCR stage, and a down-sampling stage. Each stage is only composed of SGCR layers. SGCRPose has a simple structure, a large receptive field, and a strong ability to fit complex functional relationships. On the Human3.6M dataset, this pure graph convolution method demonstrates superior 2-D-to-3-D HPE accuracy compared to state-of-the-art single-frame graph convolution approaches, while showing comparable performance to the previous best graph convolution method. Experimental results on common objects in context (COCO) confirm that SGCRPose exhibits good robustness.