We propose Geo-PIFu, a method to recover a 3D mesh from a monocular color
image of a clothed person. Our method is based on a deep implicit
function-based representation to learn latent voxel features using a
structure-aware 3D U-Net, to constrain the model in two ways: first, to resolve
feature ambiguities in query point encoding, second, to serve as a coarse human
shape proxy to regularize the high-resolution mesh and encourage global shape
regularity. We show that, by both encoding query points and constraining global
shape using latent voxel features, the reconstruction we obtain for clothed
human meshes exhibits less shape distortion and improved surface details
compared to competing methods. We evaluate Geo-PIFu on a recent human mesh
public dataset that is $10 \times$ larger than the private commercial dataset
used in PIFu and previous derivative work. On average, we exceed the state of
the art by $42.7\%$ reduction in Chamfer and Point-to-Surface Distances, and
$19.4\%$ reduction in normal estimation errors.

Geo-PIFu 是一种基于深度多层次 U-Net 结构的隐式函数的方法，通过学习隐含体素特征来从穿着衣物的人的单眼彩色图像中恢复三维网格，并通过约束模型在两个方面以提高重建效果：第一，在查询点编码中解决特征歧义；第二，在粗糙的人体形状代理中规范化高分辨率网格并鼓励全局形状规则性，实验结果表明，与竞争方法相比，Geo-PIFu 可以得到更少的形状畸变和更好的表面细节。