  In this paper, we aim to reconstruct a full 3D human shape from a single
image. Previous vertex-level and parameter regression approaches reconstruct 3D
human shape based on a pre-defined adjacency matrix to encode positive
relations between nodes. The deep topological relations for the surface of the
3D human body are not carefully exploited. Moreover, the performance of most
existing approaches often suffer from domain gap when handling more occlusion
cases in real-world scenes.
  In this work, we propose a Deep Mesh Relation Capturing Graph Convolution
Network, DC-GNet, with a shape completion task for 3D human shape
reconstruction. Firstly, we propose to capture deep relations within mesh
vertices, where an adaptive matrix encoding both positive and negative
relations is introduced. Secondly, we propose a shape completion task to learn
prior about various kinds of occlusion cases. Our approach encodes mesh
structure from more subtle relations between nodes in a more distant region.
Furthermore, our shape completion module alleviates the performance degradation
issue in the outdoor scene. Extensive experiments on several benchmarks show
that our approach outperforms the previous 3D human pose and shape estimation
approaches.
