  We present a methodology to automatically obtain Pose Space Deformation (PSD)
basis for rigged garments through deep learning. Classical approaches rely on
Physically Based Simulations (PBS) to animate clothes. These are general
solutions that, given a sufficiently fine-grained discretization of space and
time, can achieve highly realistic results. However, they are computationally
expensive and any scene modification prompts the need of re-simulation. Linear
Blend Skinning (LBS) with PSD offers a lightweight alternative to PBS, though,
it needs huge volumes of data to learn proper PSD. We propose using deep
learning, formulated as an implicit PBS, to unsupervisedly learn realistic
cloth Pose Space Deformations in a constrained scenario: dressed humans.
Furthermore, we show it is possible to train these models in an amount of time
comparable to a PBS of a few sequences. To the best of our knowledge, we are
the first to propose a neural simulator for cloth. While deep-based approaches
in the domain are becoming a trend, these are data-hungry models. Moreover,
authors often propose complex formulations to better learn wrinkles from PBS
data. Supervised learning leads to physically inconsistent predictions that
require collision solving to be used. Also, dependency on PBS data limits the
scalability of these solutions, while their formulation hinders its
applicability and compatibility. By proposing an unsupervised methodology to
learn PSD for LBS models (3D animation standard), we overcome both of these
drawbacks. Results obtained show cloth-consistency in the animated garments and
meaningful pose-dependant folds and wrinkles. Our solution is extremely
efficient, handles multiple layers of cloth, allows unsupervised outfit
resizing and can be easily applied to any custom 3D avatar.
