  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.
