In this article, we present TightCap, a data-driven scheme to capture both the human shape and dressed garments accurately with only a single three-dimensional (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 field—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 predict clothing tightness field 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 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 the high-quality human shape and dressed garments reconstruction, as well as the further applications for clothing segmentation, retargeting, and animation.

中文翻译：

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TightCap：具有服装紧度场的 3D 人体形状捕捉

在本文中，我们介绍了 TightCap，这是一种数据驱动的方案，只需一次 3D (3D) 人体扫描即可准确捕捉人体形状和穿着的服装，它支持多种应用，例如虚拟试穿、生物识别和身体评价。为了打破人体姿势和服装的严重变化，我们建议对服装紧密度场进行建模——从服装到人体形状的位移隐含在全局 UV 纹理域中。为此，我们利用增强的统计人体模板和有效的多阶段对齐方案将 3D 扫描映射到混合 2D 几何图像。基于这种 2D 表示，我们提出了一个新的框架，通过一种新的紧度公式来预测服装紧度场，以及有效的优化方案，进一步重构各种服装类别和人体姿势下的多层人体形体和服装。我们进一步提出了一个新的人体扫描服装紧密度数据集，其中包含多种服装风格、姿势和相应的真实人体形状，以刺激进一步的研究。大量实验证明了我们的 TightCap 在实现高质量的人体形状和穿着服装重建方面的有效性，以及服装分割、重定向和动画的进一步应用。