This paper addresses the problem of 3D human pose and shape estimation from a
single image. Previous approaches consider a parametric model of the human
body, SMPL, and attempt to regress the model parameters that give rise to a
mesh consistent with image evidence. This parameter regression has been a very
challenging task, with model-based approaches underperforming compared to
nonparametric solutions in terms of pose estimation. In our work, we propose to
relax this heavy reliance on the model's parameter space. We still retain the
topology of the SMPL template mesh, but instead of predicting model parameters,
we directly regress the 3D location of the mesh vertices. This is a heavy task
for a typical network, but our key insight is that the regression becomes
significantly easier using a Graph-CNN. This architecture allows us to
explicitly encode the template mesh structure within the network and leverage
the spatial locality the mesh has to offer. Image-based features are attached
to the mesh vertices and the Graph-CNN is responsible to process them on the
mesh structure, while the regression target for each vertex is its 3D location.
Having recovered the complete 3D geometry of the mesh, if we still require a
specific model parametrization, this can be reliably regressed from the
vertices locations. We demonstrate the flexibility and the effectiveness of our
proposed graph-based mesh regression by attaching different types of features
on the mesh vertices. In all cases, we outperform the comparable baselines
relying on model parameter regression, while we also achieve state-of-the-art
results among model-based pose estimation approaches.

本文解决从单张图像中估计 3D 人体姿态和形状的问题，提出在保留 SMPL 模板网格拓扑的同时，使用 Graph-CNN 直接回归网格顶点的三维位置，与基于模型参数回归的可比较基线相比，我们的方法具有更好的效果。