  The fields of SocialVR, performance capture, and virtual try-on are often
faced with a need to faithfully reproduce real garments in the virtual world.
One critical task is the disentanglement of the intrinsic garment shape from
deformations due to fabric properties, physical forces, and contact with the
body. We propose to use a garment sewing pattern, a realistic and compact
garment descriptor, to facilitate the intrinsic garment shape estimation.
Another major challenge is a high diversity of shapes and designs in the
domain. The most common approach for Deep Learning on 3D garments is to build
specialized models for individual garments or garment types. We argue that
building a unified model for various garment designs has the benefit of
generalization to novel garment types, hence covering a larger design domain
than individual models would. We introduce NeuralTailor, a novel architecture
based on point-level attention for set regression with variable cardinality,
and apply it to the task of reconstructing 2D garment sewing patterns from the
3D point could garment models. Our experiments show that NeuralTailor
successfully reconstructs sewing patterns and generalizes to garment types with
pattern topologies unseen during training.
