Skin Tone Disentanglement in 2D Makeup Transfer With Graph Neural Networks
Masoud Mokhtari, Fatemeh Taheri Dezaki, Timo Bolkart, Betty Mohler Tesch, Rahul Suresh, Amin Banitalebi-Dehkordi
Abstract
Makeup transfer involves transferring makeup from a reference image to a target image while maintaining the target’s identity. Existing methods, which use Generative Adversarial Networks, often transfer not just makeup but also the reference image’s skin tone. This limits their use to similar skin tones and introduces bias. Our solution introduces a skin tone-robust makeup embedding achieved by augmenting the reference image with varied skin tones. Using Graph Neural Networks, we establish connections between target, reference, and augmented images to create this robust representation that preserves the target’s skin tone. In a user study, our approach outperformed other methods 66% of the time, showcasing its resilience to skin tone variations.
BibTeX
@inproceedings{icassp2024_skintonedisentan,
title = {Skin Tone Disentanglement in 2D Makeup Transfer With Graph Neural Networks},
author = {Masoud Mokhtari and Fatemeh Taheri Dezaki and Timo Bolkart and Betty Mohler Tesch and Rahul Suresh and Amin Banitalebi-Dehkordi},
booktitle = {ICASSP 2024},
year = {2024}
}