RESEARCH

COMPUTER VISION

ViBE: Dressing for Diverse Body Shapes

April 01, 2020

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 status quo body-agnostic recommendation, both according to automated metrics and human opinion.

Download the Paper

AUTHORS

Written by

Kristen Grauman

Kimberly Hsiao

Publisher

CVPR

Research Topics

Computer Vision

Related Publications

March 09, 2023

COMPUTER VISION

The Casual Conversations v2 Dataset

Bilal Porgali, Vítor Albiero, Jordan Ryda, Cristian Canton Ferrer, Caner Hazirbas

March 09, 2023

February 21, 2023

COMPUTER VISION

CORE MACHINE LEARNING

ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks

Felix Xu, Fuyuan Zhang, Hua Qi, Jianjun Zhao, Jianlang Chen, Lei Ma, Qing Guo, Zhijie Wang

February 21, 2023

January 10, 2023

COMPUTER VISION

CORE MACHINE LEARNING

Online Backfilling with No Regret for Large-Scale Image Retrieval

Gokhan Uzunbas, Joena Zhang, Sara Cao, Ser-Nam Lim, Taipeng Tian, Bohyung Han, Seonguk Seo

January 10, 2023

January 04, 2023

COMPUTER VISION

CORE MACHINE LEARNING

Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

Xi Liu, Panganamala Kumar, Ruida Zhou, Tao Liu

January 04, 2023

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.