  We present a self-supervised method to learn dynamic 3D deformations of
garments worn by parametric human bodies. State-of-the-art data-driven
approaches to model 3D garment deformations are trained using supervised
strategies that require large datasets, usually obtained by expensive
physics-based simulation methods or professional multi-camera capture setups.
In contrast, we propose a new training scheme that removes the need for
ground-truth samples, enabling self-supervised training of dynamic 3D garment
deformations. Our key contribution is to realize that physics-based deformation
models, traditionally solved in a frame-by-frame basis by implicit integrators,
can be recasted as an optimization problem. We leverage such optimization-based
scheme to formulate a set of physics-based loss terms that can be used to train
neural networks without precomputing ground-truth data. This allows us to learn
models for interactive garments, including dynamic deformations and fine
wrinkles, with two orders of magnitude speed up in training time compared to
state-of-the-art supervised methods
