GMDN: A lightweight graph-based mixture density network for 3D human pose regression

3D human pose estimation from 2D detections is an ill-posed problem because multiple solutions may exist due to the inherent ambiguity and occlusion. In this paper, we propose a novel graph-based mixture density network (GMDN) to tackle the 2D-to-3D human pose estimation problem. We formulate the 2D joint locations of the human body as a graph, and thus the pose estimation task can be redefined as a graph regression problem. Additionally, we present a novel graph convolutional operation with the incorporation of structural knowledge about human body configurations to assist with reasoning of the structural relations implied in the human bodies. Furthermore, we employ mixture density networks to formulate the 3D human poses as a multimodal distribution. The presented GMDN is lightweight with only 0.30M parameters, and the experimental results demonstrate that it achieves state-of-the-art performance. ? 2021 Elsevier Ltd. All rights reserved. 3D human pose estimation from 2D detections is an ill-posed problem because multiple solutions may exist due to the inherent ambiguity and occlusion. In this paper, we propose a novel graph-based mixture density network (GMDN) to tackle the 2D-to-3D human pose estimation problem. We formulate the 2D joint locations of the human body as a graph, and thus the pose estimation task can be redefined as a graph regression problem. Additionally, we present a novel graph convolutional operation with the incorporation of structural knowledge about human body configurations to assist with reasoning of the structural relations implied in the human bodies. Furthermore, we employ mixture density networks to formulate the 3D human poses as a multimodal distribution. The presented GMDN is lightweight with only 0.30M parameters, and the experimental results demonstrate that it achieves state-of-the-art performance.