  Nonparametric approaches have shown promising results on reconstructing 3D
human mesh from a single monocular image. Unlike previous approaches that use a
parametric human model like skinned multi-person linear model (SMPL), and
attempt to regress the model parameters, nonparametric approaches relax the
heavy reliance on the parametric space. However, existing nonparametric methods
require ground truth meshes as their regression target for each vertex, and
obtaining ground truth mesh labels is very expensive. In this paper, we propose
a novel approach to learn human mesh reconstruction without any ground truth
meshes. This is made possible by introducing two new terms into the loss
function of a graph convolutional neural network (Graph CNN). The first term is
the Laplacian prior that acts as a regularizer on the reconstructed mesh. The
second term is the part segmentation loss that forces the projected region of
the reconstructed mesh to match the part segmentation. Experimental results on
multiple public datasets show that without using 3D ground truth meshes, the
proposed approach outperforms the previous state-of-the-art approaches that
require ground truth meshes for training.
