This paper presents a learning-based clothing animation method for highly
efficient virtual try-on simulation. Given a garment, we preprocess a rich
database of physically-based dressed character simulations, for multiple body
shapes and animations. Then, using this database, we train a learning-based
model of cloth drape and wrinkles, as a function of body shape and dynamics. We
propose a model that separates global garment fit, due to body shape, from
local garment wrinkles, due to both pose dynamics and body shape. We use a
recurrent neural network to regress garment wrinkles, and we achieve highly
plausible nonlinear effects, in contrast to the blending artifacts suffered by
previous methods. At runtime, dynamic virtual try-on animations are produced in
just a few milliseconds for garments with thousands of triangles. We show
qualitative and quantitative analysis of results

本文提出了一种基于学习的服装动画方法，用于高效的虚拟试穿模拟，其中使用了基于物理的模拟，基于数据库的模型训练，并将全局服装吻合和局部服装皱纹分离。同时，使用循环神经网络来回归服装皱纹，实现了高度逼真的非线性效果。