  We present a learning-based approach for virtual try-on applications based on
a fully convolutional graph neural network. In contrast to existing data-driven
models, which are trained for a specific garment or mesh topology, our fully
convolutional model can cope with a large family of garments, represented as
parametric predefined 2D panels with arbitrary mesh topology, including long
dresses, shirts, and tight tops. Under the hood, our novel geometric deep
learning approach learns to drape 3D garments by decoupling the three different
sources of deformations that condition the fit of clothing: garment type,
target body shape, and material. Specifically, we first learn a regressor that
predicts the 3D drape of the input parametric garment when worn by a mean body
shape. Then, after a mesh topology optimization step where we generate a
sufficient level of detail for the input garment type, we further deform the
mesh to reproduce deformations caused by the target body shape. Finally, we
predict fine-scale details such as wrinkles that depend mostly on the garment
material. We qualitatively and quantitatively demonstrate that our fully
convolutional approach outperforms existing methods in terms of generalization
capabilities and memory requirements, and therefore it opens the door to more
general learning-based models for virtual try-on applications.
