  Recent advancements in deep learning have enabled 3D human body
reconstruction from a monocular image, which has broad applications in multiple
domains. In this paper, we propose SHARP (SHape Aware Reconstruction of People
in loose clothing), a novel end-to-end trainable network that accurately
recovers the 3D geometry and appearance of humans in loose clothing from a
monocular image. SHARP uses a sparse and efficient fusion strategy to combine
parametric body prior with a non-parametric 2D representation of clothed
humans. The parametric body prior enforces geometrical consistency on the body
shape and pose, while the non-parametric representation models loose clothing
and handle self-occlusions as well. We also leverage the sparseness of the
non-parametric representation for faster training of our network while using
losses on 2D maps. Another key contribution is 3DHumans, our new life-like
dataset of 3D human body scans with rich geometrical and textural details. We
evaluate SHARP on 3DHumans and other publicly available datasets and show
superior qualitative and quantitative performance than existing
state-of-the-art methods.
