  We present a novel learning framework for cloth deformation by embedding
virtual cloth into a tetrahedral mesh that parametrizes the volumetric region
of air surrounding the underlying body. In order to maintain this volumetric
parameterization during character animation, the tetrahedral mesh is
constrained to follow the body surface as it deforms. We embed the cloth mesh
vertices into this parameterization of three-dimensional space in order to
automatically capture much of the nonlinear deformation due to both joint
rotations and collisions. We then train a convolutional neural network to
recover ground truth deformation by learning cloth embedding offsets for each
skeletal pose. Our experiments show significant improvement over learning cloth
offsets from body surface parameterizations, both quantitatively and visually,
with prior state of the art having a mean error five standard deviations higher
than ours. Moreover, our results demonstrate the efficacy of a general learning
paradigm where high-frequency details can be embedded into low-frequency
parameterizations.
