While modeling people wearing tight-fitting clothing has made great strides in recent years, loose-fitting clothing remains a challenge. We propose a method that delivers realistic garment models from real-world images, regardless of garment shape or deformation. To this end, we introduce a fitting approach that utilizes shape and deformation priors learned from synthetic data to accurately capture garment shapes and deformations, including large ones. Not only does our approach recover the garment geometry accurately, it also yields models that can be directly used by downstream applications such as animation and simulation.

翻译：近年来，紧身衣物的建模取得了显著进展，但宽松衣物仍是一项挑战。我们提出一种方法，可从真实世界图像中生成逼真的服装模型，无论其形状或变形如何。为此，我们引入一种拟合方法，利用从合成数据中学习到的形状与变形先验，精准捕捉服装的形状和变形（包括大幅变形）。我们的方法不仅能准确恢复服装几何结构，还能生成可直接用于动画、仿真等下游应用的模型。