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：提出互补服装单品以组成一套搭配是一个新兴的研究方向，但需要对时尚趋势和视觉美学有精细的理解。以往研究主要集中于通过评分视觉吸引力、将衣物表示为有序序列或成对兼容物品集合来进行推荐，这限制了衣物间关系的充分利用。我们试图通过利用物品集合的图表示来弥合服装搭配推荐与生成之间的差距。本文工作致力于从现有搭配集合中发现新的吸引力搭配，从而搭建推荐与生成之间的桥梁。我们提出一种基于Transformer的架构——TGNN，该架构利用多头自注意力机制，在卷积图神经网络中将衣物之间的图关系作为消息传递步骤进行捕获。具体而言，从一个或多个衣物种子出发，通过迭代选择与先前所选衣物最兼容的衣物来实现服装搭配生成。我们在两个不同数据集上进行了大量实验，证明了模型在执行种子引导的服装搭配生成方面的能力，并在兼容性评估任务上取得了当前最优的结果。