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 this https URL

该论文提出了一种新的框架，利用深度神经网络和 Hierarchical Mesh Deformation 技术综合使用人体关节、轮廓和像素的着色信息，从而在单张图片中精确恢复人体形状。实验证明，我们的方法在 2D IoU 计算和 3D 距离计算的准确度上都优于现有的其他方法。