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.

我们提出了一种从现实世界图像中生成逼真服装模型的方法，不论服装的形状或变形如何，该方法利用从合成数据中学习的形状和变形先验准确地捕捉服装的形状和变形，包括较大的变形，并直接应用于动画和模拟等下游应用。

服装恢复与形状和变形先验

Garment Recovery with Shape and Deformation Priors

Fashion manipulation has attracted growing interest due to its great
application value, which inspires many researches towards fashion images.
However, little attention has been paid to fashion design draft. In this paper,
we study a new unaligned translation problem between design drafts and real
fashion items, whose main challenge lies in the huge misalignment between the
two modalities. We first collect paired design drafts and real fashion item
images without pixel-wise alignment. To solve the misalignment problem, our
main idea is to train a sampling network to adaptively adjust the input to an
intermediate state with structure alignment to the output. Moreover, built upon
the sampling network, we present design draft to real fashion item translation
network (D2RNet), where two separate translation streams that focus on texture
and shape, respectively, are combined tactfully to get both benefits. D2RNet is
able to generate realistic garments with both texture and shape consistency to
their design drafts. We show that this idea can be effectively applied to the
reverse translation problem and present R2DNet accordingly. Extensive
experiments on unaligned fashion design translation demonstrate the superiority
of our method over state-of-the-art methods. Our project website is available
at: this https URL .

本文利用采样网络建立设计草稿与真实时装的翻译模型，实现设计草稿向真实时装的转化以及真实时装向设计草稿的转化，并通过实验证明了该模型的卓越性能。