  Suggesting complementary clothing items to compose an outfit is a process of
emerging interest, yet it involves a fine understanding of fashion trends and
visual aesthetics. Previous works have mainly focused on recommendation by
scoring visual appeal and representing garments as ordered sequences or as
collections of pairwise-compatible items. This limits the full usage of
relations among clothes. We attempt to bridge the gap between outfit
recommendation and generation by leveraging a graph-based representation of
items in a collection. The work carried out in this paper, tries to build a
bridge between outfit recommendation and generation, by discovering new
appealing outfits starting from a collection of pre-existing ones. We propose a
transformer-based architecture, named TGNN, which exploits multi-headed self
attention to capture relations between clothing items in a graph as a message
passing step in Convolutional Graph Neural Networks. Specifically, starting
from a seed, i.e.~one or more garments, outfit generation is performed by
iteratively choosing the garment that is most compatible with the previously
chosen ones. Extensive experimentations are conducted with two different
datasets, demonstrating the capability of the model to perform seeded outfit
generation as well as obtaining state of the art results on compatibility
estimation tasks.
