OutfitGAN: Learning Compatible Items for Generative Fashion Outfits

Maryam Moosaei, Yusan Lin, Ablaikhan Akhazhanov, Huiyuan Chen, Fei Wang, Hao Yang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 2273-2277

Abstract

Fashion-on-demand is becoming an important concept for fashion industries. Many attempts have been made to leverage machine learning methods to generate fashion designs tailored to customers' tastes. However, how to assemble items together (e.g., compatibility) is crucial in designing high-quality outfits for synthesis images. Here we propose a fashion generation model, named OutfitGAN, which contains two core modules: a Generative Adversarial Network and a Compatibility Network. The generative module is able to generate new realistic high quality fashion items from a specific category, while the compatibility network ensures reasonable compatibility among all items. The experimental results show the superiority of our OutfitGAN.

Related Material

[pdf]
[bibtex]
@InProceedings{Moosaei_2022_CVPR, author = {Moosaei, Maryam and Lin, Yusan and Akhazhanov, Ablaikhan and Chen, Huiyuan and Wang, Fei and Yang, Hao}, title = {OutfitGAN: Learning Compatible Items for Generative Fashion Outfits}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {2273-2277} }