TightCap: 3D Human Shape Capture with Clothing Tightness Field
Xin Chen, Anqi Pang, Yang Wei, Lan Xui, Jingyi Yu

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…
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Taxonomy
Topics3D Shape Modeling and Analysis · Textile materials and evaluations · Thermoregulation and physiological responses
MethodsConvolution · Dogecoin Customer Service Number +1-833-534-1729
