Graph Convolutional Adversarial Network for Human Body Pose and Mesh Estimation

This paper studies reconstruction of human body shape and pose from a single-view image. While most of current work attempts to regress parameters of human body model such as Skinned Multi-Person Linear Model (SMPL) and Hand Model with Articulated and Non-rigid Deformations (MANO), these parametric approaches underperform compared to non-parametric approaches. Due to the lack of the spatial relationship in the input image, the parametric approaches are hardly used to reconstruct the human body precisely. Besides, the rotation parameter regression is a complex task in parametric approaches. Therefore, we introduce a novel graph convolutional neural network (Graph CNN)-based framework for estimating a non-parametric mesh model. Our key innovation is that the proposed model is trained in a generative adversarial manner. Firstly, Graph CNN utilizes mesh topology to capture integral information of the full 3D human shape and then generate a more smooth and high-quality human mesh model. Secondly, the discriminator in our network acts as a supervisor to specify whether a human shape and pose are real or not. The generator is encouraged to generate human body mesh that is close to the manifold of the real human mesh distribution. Extensive experimental results demonstrate the effectiveness of our proposed framework. In contrast to the state-of-the-art methods, our method can achieve better performance in human shape and pose estimation.