  This paper tackles the problem of estimating 3D body shape of clothed humans
from single polarized 2D images, i.e. polarization images. Polarization images
are known to be able to capture polarized reflected lights that preserve rich
geometric cues of an object, which has motivated its recent applications in
reconstructing surface normal of the objects of interest. Inspired by the
recent advances in human shape estimation from single color images, in this
paper, we attempt at estimating human body shapes by leveraging the geometric
cues from single polarization images. A dedicated two-stage deep learning
approach, SfP, is proposed: given a polarization image, stage one aims at
inferring the fined-detailed body surface normal; stage two gears to
reconstruct the 3D body shape of clothing details. Empirical evaluations on a
synthetic dataset (SURREAL) as well as a real-world dataset (PHSPD) demonstrate
the qualitative and quantitative performance of our approach in estimating
human poses and shapes. This indicates polarization camera is a promising
alternative to the more conventional color or depth imaging for human shape
estimation. Further, normal maps inferred from polarization imaging play a
significant role in accurately recovering the body shapes of clothed people.
