We present a novel method to generate accurate and realistic clothing
deformation from real data capture. Previous methods for realistic cloth
modeling mainly rely on intensive computation of physics-based simulation (with
numerous heuristic parameters), while models reconstructed from visual
observations typically suffer from lack of geometric details. Here, we propose
an original framework consisting of two modules that work jointly to represent
global shape deformation as well as surface details with high fidelity. Global
shape deformations are recovered from a subspace model learned from 3D data of
clothed people in motion, while high frequency details are added to normal maps
created using a conditional Generative Adversarial Network whose architecture
is designed to enforce realism and temporal consistency. This leads to
unprecedented high-quality rendering of clothing deformation sequences, where
fine wrinkles from (real) high resolution observations can be recovered. In
addition, as the model is learned independently from body shape and pose, the
framework is suitable for applications that require retargeting (e.g., body
animation). Our experiments show original high quality results with a flexible
model. We claim an entirely data-driven approach to realistic cloth wrinkle
generation is possible.

本研究提出了一种数据驱动的方法，使用条件生成对抗网络来实现真实服装的高保真度仿真，可用于身体动画等应用。