  We introduce PhysXNet, a learning-based approach to predict the dynamics of
deformable clothes given 3D skeleton motion sequences of humans wearing these
clothes. The proposed model is adaptable to a large variety of garments and
changing topologies, without need of being retrained. Such simulations are
typically carried out by physics engines that require manual human expertise
and are subjectto computationally intensive computations. PhysXNet, by
contrast, is a fully differentiable deep network that at inference is able to
estimate the geometry of dense cloth meshes in a matter of milliseconds, and
thus, can be readily deployed as a layer of a larger deep learning
architecture. This efficiency is achieved thanks to the specific
parameterization of the clothes we consider, based on 3D UV maps encoding
spatial garment displacements. The problem is then formulated as a mapping
between the human kinematics space (represented also by 3D UV maps of the
undressed body mesh) into the clothes displacement UV maps, which we learn
using a conditional GAN with a discriminator that enforces feasible
deformations. We train simultaneously our model for three garment templates,
tops, bottoms and dresses for which we simulate deformations under 50 different
human actions. Nevertheless, the UV map representation we consider allows
encapsulating many different cloth topologies, and at test we can simulate
garments even if we did not specifically train for them. A thorough evaluation
demonstrates that PhysXNet delivers cloth deformations very close to those
computed with the physical engine, opening the door to be effectively
integrated within deeplearning pipelines.
