  This paper presents a novel framework to recover detailed human body shapes
from a single image. It is a challenging task due to factors such as variations
in human shapes, body poses, and viewpoints. Prior methods typically attempt to
recover the human body shape using a parametric based template that lacks the
surface details. As such the resulting body shape appears to be without
clothing. In this paper, we propose a novel learning-based framework that
combines the robustness of parametric model with the flexibility of free-form
3D deformation. We use the deep neural networks to refine the 3D shape in a
Hierarchical Mesh Deformation (HMD) framework, utilizing the constraints from
body joints, silhouettes, and per-pixel shading information. We are able to
restore detailed human body shapes beyond skinned models. Experiments
demonstrate that our method has outperformed previous state-of-the-art
approaches, achieving better accuracy in terms of both 2D IoU number and 3D
metric distance. The code is available in https://github.com/zhuhao-nju/hmd.git
