ViBE: Dressing for Diverse Body Shapes

TL;DR

ViBE introduces a visual embedding that personalizes clothing recommendations based on individual body shapes, improving fit and inclusivity over traditional one-size-fits-all methods.

Contribution

The paper presents ViBE, a novel body-aware embedding learned from diverse fashion images, enabling personalized clothing suggestions that account for individual body shapes.

Findings

Outperforms body-agnostic methods in automated metrics

Receives higher human approval ratings

Effectively identifies well-fitting garments for diverse body types

Abstract

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.