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.

翻译：提出了一种新的装扮生成方法，旨在从现有的装扮合集中发现新的和谐搭配，并建立装扮推荐和生成之间的连接。本文提出了一种基于Transformer的架构，称为TGNN，通过使用多头自注意力，将图神经网络中的信息传递步骤应用于捕捉服装项之间的关系。通过从种子开始（即一个或多个服装），通过选择与先前选择的服装最兼容的服装，执行装扮生成。通过两个不同的数据集进行了广泛的实验，证明该模型具有执行以种子为基础的装扮生成的能力，并在兼容性估计任务上获得了最先进的结果。