  This paper focuses on a new problem of estimating human pose and shape from
single polarization images. Polarization camera is known to be able to capture
the polarization of reflected lights that preserves rich geometric cues of an
object surface. Inspired by the recent applications in surface normal
reconstruction from polarization images, in this paper, we attempt to estimate
human pose and shape from single polarization images by leveraging the
polarization-induced geometric cues. A dedicated two-stage pipeline is
proposed: given a single polarization image, stage one (Polar2Normal) focuses
on the fine detailed human body surface normal estimation; stage two
(Polar2Shape) then reconstructs clothed human shape from the polarization image
and the estimated surface normal. To empirically validate our approach, a
dedicated dataset (PHSPD) is constructed, consisting of over 500K frames with
accurate pose and parametric shape annotations. Empirical evaluations on this
real-world dataset as well as a synthetic dataset, SURREAL, demonstrate the
effectiveness of our approach. It suggests polarization camera as a promising
alternative to the more conventional RGB camera for human pose and shape
estimation.
