Many approaches to draping individual garments on human body models are
realistic, fast, and yield outputs that are differentiable with respect to the
body shape on which they are draped. However, none of them can handle
multi-layered clothing, which is prevalent in everyday dress. In this paper, we
introduce a parametric garment representation model that can. As in models used
by clothing designers, each garment consists of individual 2D panels. Their 2D
shape is defined by a Signed Distance Function and 3D shape by a 2D to 3D
mapping. The 2D parameterization enables easy detection of potential collisions
and the 3D parameterization handles complex shapes effectively. We show that
this combination is faster and yields higher quality reconstructions than
purely implicit surface representations, and makes the recovery of layered
garments from images possible thanks to its differentiability. Furthermore, it
supports rapid editing of garment shapes and texture by modifying individual 2D
panels.

本文介绍一种参数化服装表示模型，它可以处理多层服装，并且使用 2D 和 3D 参数化定义服装形状来实现不同 iability，这种组合比纯隐式表面表示更快，重建结果更高质量，并且支持通过修改 2D 面材质实现快速编辑服装形状。

ISP: 多层服装褶皱设计与隐式缝合图案

ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns

Garment representation, editing and animation are challenging topics in the
area of computer vision and graphics. It remains difficult for existing garment
representations to achieve smooth and plausible transitions between different
shapes and topologies. In this work, we introduce, DeepCloth, a unified
framework for garment representation, reconstruction, animation and editing.
Our unified framework contains 3 components: First, we represent the garment
geometry with a "topology-aware UV-position map", which allows for the unified
description of various garments with different shapes and topologies by
introducing an additional topology-aware UV-mask for the UV-position map.
Second, to further enable garment reconstruction and editing, we contribute a
method to embed the UV-based representations into a continuous feature space,
which enables garment shape reconstruction and editing by optimization and
control in the latent space, respectively. Finally, we propose a garment
animation method by unifying our neural garment representation with body shape
and pose, which achieves plausible garment animation results leveraging the
dynamic information encoded by our shape and style representation, even under
drastic garment editing operations. To conclude, with DeepCloth, we move a step
forward in establishing a more flexible and general 3D garment digitization
framework. Experiments demonstrate that our method can achieve state-of-the-art
garment representation performance compared with previous methods.

本文提出了 DeepCloth，它是一个针对服装表示、重建、动画和编辑的统一框架，它包含了三个组件：拓扑感知的 UV 位置图、连续特征空间中的嵌入和服装动画方法。这个方法可以实现更加灵活、通用的 3D 服装数字化框架。