ViBE: Dressing for Diverse Body Shapes
This addresses the inclusion barrier in fashion recommendation for diverse body shapes, representing a domain-specific incremental improvement.
The paper tackles the problem of body-agnostic clothing recommendations by introducing ViBE, a visual body-aware embedding that identifies flattering garments for specific body shapes, demonstrating strong advantages over existing methods in automated metrics and human evaluations.
Body shape plays an important role in determining what garments will best suit a given person, yet today's clothing recommendation methods take a "one shape fits all" approach. These body-agnostic vision methods and datasets are a barrier to inclusion, ill-equipped to provide good suggestions for diverse body shapes. We introduce ViBE, a VIsual Body-aware Embedding that captures clothing's affinity with different body shapes. Given an image of a person, the proposed embedding identifies garments that will flatter her specific body shape. We show how to learn the embedding from an online catalog displaying fashion models of various shapes and sizes wearing the products, and we devise a method to explain the algorithm's suggestions for well-fitting garments. We apply our approach to a dataset of diverse subjects, and demonstrate its strong advantages over the status quo body-agnostic recommendation, both according to automated metrics and human opinion.