  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
