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.

翻译：在本文中,我们展示了TightCap, 这是一种由数据驱动的计划,以精确地捕捉人的形状和着装,只进行单一的3D人体扫描,从而精确地捕捉人的形状和服装,这种由数据驱动的计划,能够使虚拟试镜、生物测定和身体评估等多种应用成为可能。为了打破人脸和服装的严重差异,我们提议模拟衣物紧凑性,即全球紫外线纹域内从服装向人形状的隐含偏移。为此,我们利用一个强化的统计人类模板和一个有效的多阶段协调计划,将3D扫描映射成混合的 2D 几何图象。基于这一2D 代表,我们提出了一个新的框架,通过一种新的紧身配方来预测衣紧身,以及一个有效的优化计划,以进一步重建不同服装类别和人类姿势下的多层人类形状和服装。我们进一步提出了一个新的衣物紧身数据集。我们提出了一个新的服装紧身数据集(CTD),以各种服装样式、姿势和相应的地面图象来刺激进一步的研究。广泛的实验展示了我们紧身结构的效能,以达到高质的形状和服装的整整形和服装结构。