April 01, 2020
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.
March 09, 2023
Bilal Porgali, Vítor Albiero, Jordan Ryda, Cristian Canton Ferrer, Caner Hazirbas
March 09, 2023
February 21, 2023
Felix Xu, Fuyuan Zhang, Hua Qi, Jianjun Zhao, Jianlang Chen, Lei Ma, Qing Guo, Zhijie Wang
February 21, 2023
January 10, 2023
Gokhan Uzunbas, Joena Zhang, Sara Cao, Ser-Nam Lim, Taipeng Tian, Bohyung Han, Seonguk Seo
January 10, 2023
January 04, 2023
Xi Liu, Panganamala Kumar, Ruida Zhou, Tao Liu
January 04, 2023