# ViBE: Dressing for Diverse Body Shapes

arXiv: https://arxiv.org/abs/1912.06697 · HTML: https://mlsanity.com/paper/1912.06697/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

## Built on by (most cited first, 11 of 11)

- [Accurate 3D Body Shape Regression using Metric and Semantic Attributes](https://mlsanity.com/paper/2206.07036/) (2022)
- [Semi-supervised Synthesis of High-Resolution Editable Textures for 3D Humans](https://mlsanity.com/paper/2103.17266/) (2021)
- [From Culture to Clothing: Discovering the World Events Behind A Century of Fashion Images](https://mlsanity.com/paper/2102.01690/) (2021)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [FitGAN: Fit- and Shape-Realistic Generative Adversarial Networks for Fashion](https://mlsanity.com/paper/2206.11768/) (2022)
- [Modern Augmented Reality: Applications, Trends, and Future Directions](https://mlsanity.com/paper/2202.09450/) (2022)
- [SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce](https://mlsanity.com/paper/2106.03532/) (2021)
- [Incorporating Customer Reviews in Size and Fit Recommendation systems for Fashion E-Commerce](https://mlsanity.com/paper/2208.06261/) (2022)
- [Fashionpedia-Taste: A Dataset towards Explaining Human Fashion Taste](https://mlsanity.com/paper/2305.02307/) (2023)
- [Fashionpedia-Ads: Do Your Favorite Advertisements Reveal Your Fashion Taste?](https://mlsanity.com/paper/2305.02360/) (2023)
- [DL-EWF: Deep Learning Empowering Women's Fashion with Grounded-Segment-Anything Segmentation for Body Shape Classification](https://mlsanity.com/paper/2404.04891/) (2024)

## Abstract (arXiv)

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.