  Recently there has been a significant effort to automate UV mapping, the
process of mapping 3D-dimensional surfaces to the UV space while minimizing
distortion and seam length. Although state-of-the-art methods, Autocuts and
OptCuts, addressed this task via energy-minimization approaches, they fail to
produce semantic seam styles, an essential factor for professional artists. The
recent emergence of Graph Neural Networks (GNNs), and the fact that a mesh can
be represented as a particular form of a graph, has opened a new bridge to
novel graph learning-based solutions in the computer graphics domain. In this
work, we use the power of supervised GNNs for the first time to propose a fully
automated UV mapping framework that enables users to replicate their desired
seam styles while reducing distortion and seam length. To this end, we provide
augmentation and decimation tools to enable artists to create their dataset and
train the network to produce their desired seam style. We provide a
complementary post-processing approach for reducing the distortion based on
graph algorithms to refine low-confidence seam predictions and reduce seam
length (or the number of shells in our supervised case) using a skeletonization
method.
