Learning Visual Body-shape-Aware Embeddings for Fashion Compatibility

Body shape is a crucial factor in outfit recommendation. Previous studies that directly used body measurement data to investigate the relationship between body shape and outfit have achieved limited performance due to oversimplified body shape representations. This paper proposes a Visual Body-shape-Aware Network (ViBA-Net) to improve the fashion compatibility model's awareness of human body shape through visual-level information. Specifically, ViBA-Net consists of three modules: a body-shape embedding module, which extracts visual and anthropometric features of body shape from a newly introduced large-scale body shape dataset; an outfit embedding module, which learns the outfit representation based on visual features extracted from a try-on image and textual features extracted from fashion attributes; and a joint embedding module, which jointly models the relationship between the representations of body shape and outfit. ViBA-Net is designed to generate attribute-level explanations for the evaluation results based on the computed attention weights. The effectiveness of ViBA-Net is evaluated on two mainstream datasets through qualitative and quantitative analysis. Data and code are released https://github.com/BenjaminPang/ViBA-Net.