TightCap: 3D Human Shape Capture with Clothing Tightness Field

TL;DR

TightCap is a data-driven method that accurately captures 3D human shape and clothing from a single scan by modeling clothing tightness in a 2D domain, enabling various applications.

Contribution

The paper introduces a novel clothing tightness modeling framework and a new dataset, improving 3D human shape and garment reconstruction from minimal input.

Findings

High-quality shape and garment reconstruction achieved

Effective modeling of clothing tightness in 2D domain

Versatile applications including segmentation and animation

Abstract

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.