Existing data-driven methods for garment animation, usually driven by linear
skinning, although effective on tight garments, do not handle loose-fitting
garments with complex deformations well. To address these limitations, we
develop a garment generative model based on deformation decomposition to
efficiently simulate loose garment deformation without directly using linear
skinning. Specifically, we learn a garment generative space with the proposed
generative model, where we decouple the latent representation into unposed
deformed garments and dynamic offsets during the decoding stage. With explicit
garment deformations decomposition, our generative model is able to generate
complex pose-driven deformations on canonical garment shapes. Furthermore, we
learn to transfer the body motions and previous state of the garment to the
latent space to regenerate dynamic results. In addition, we introduce a detail
enhancement module in an adversarial training setup to learn high-frequency
wrinkles. We demonstrate our method outperforms state-of-the-art data-driven
alternatives through extensive experiments and show qualitative and
quantitative analysis of results.

基于变形分解的服装生成模型，能够高效地模拟松散服装的变形，生成带有复杂姿势驱动的变形效果，并通过对高频皱纹的增强模块进行反对抗训练，实验结果表明该方法优于现有的数据驱动方法。

通过变形分解生成模型实现宽松服装动画

Towards Loose-Fitting Garment Animation via Generative Model of  Deformation Decomposition

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。

DeepCloth: 基于神经网络的服装表征方法用于形状和风格编辑

DeepCloth: Neural Garment Representation for Shape and Style Editing

We present a novel solution to the garment animation problem through deep
learning. Our contribution allows animating any template outfit with arbitrary
topology and geometric complexity. Recent works develop models for garment
edition, resizing and animation at the same time by leveraging the support body
model (encoding garments as body homotopies). This leads to complex engineering
solutions that suffer from scalability, applicability and compatibility. By
limiting our scope to garment animation only, we are able to propose a simple
model that can animate any outfit, independently of its topology, vertex order
or connectivity. Our proposed architecture maps outfits to animated 3D models
into the standard format for 3D animation (blend weights and blend shapes
matrices), automatically providing of compatibility with any graphics engine.
We also propose a methodology to complement supervised learning with an
unsupervised physically based learning that implicitly solves collisions and
enhances cloth quality.

本研究通过深度学习提出了一种解决服装动画问题的新方法，可以对任何拓扑和几何复杂度的模板服装进行动画处理，并提出了一种补充有监督学习的无监督物理学习方法，以隐式解决碰撞和增强布料质量。