  3D human body reconstruction from monocular images is an interesting and
ill-posed problem in computer vision with wider applications in multiple
domains. In this paper, we propose SHARP, a novel end-to-end trainable network
that accurately recovers the detailed geometry and appearance of 3D people in
loose clothing from a monocular image. We propose a sparse and efficient fusion
of a parametric body prior with a non-parametric peeled depth map
representation of clothed models. The parametric body prior constraints our
model in two ways: first, the network retains geometrically consistent body
parts that are not occluded by clothing, and second, it provides a body shape
context that improves prediction of the peeled depth maps. This enables SHARP
to recover fine-grained 3D geometrical details with just L1 losses on the 2D
maps, given an input image. We evaluate SHARP on publicly available Cloth3D and
THuman datasets and report superior performance to state-of-the-art approaches.
