  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.
