  In this paper, we present TightCap, a data-driven scheme to capture both the
human shape and dressed garments accurately with only a single 3D human scan,
which enables numerous applications such as virtual try-on, biometrics and body
evaluation. To break the severe variations of the human poses and garments, we
propose to model the clothing tightness - the displacements from the garments
to the human shape implicitly in the global UV texturing domain. To this end,
we utilize an enhanced statistical human template and an effective multi-stage
alignment scheme to map the 3D scan into a hybrid 2D geometry image. Based on
this 2D representation, we propose a novel framework to predicted clothing
tightness via a novel tightness formulation, as well as an effective
optimization scheme to further reconstruct multi-layer human shape and garments
under various clothing categories and human postures. We further propose a new
clothing tightness dataset (CTD) of human scans with a large variety of
clothing styles, poses and corresponding ground-truth human shapes to stimulate
further research. Extensive experiments demonstrate the effectiveness of our
TightCap to achieve high-quality human shape and dressed garments
reconstruction, as well as the further applications for clothing segmentation,
retargeting and animation.
