Related papers: Face Identity-Aware Disentanglement in StyleGAN

Face Identity-Aware Disentanglement in StyleGAN

URL: http://arxiv.org/abs/2309.12033v1
Date: Thu, 21 Sep 2023 12:54:09 GMT
Title: Face Identity-Aware Disentanglement in StyleGAN
Authors: Adrian Suwa{\l}a, Bartosz W\'ojcik, Magdalena Proszewska, Jacek Tabor, Przemys{\l}aw Spurek, Marek \'Smieja
Abstract summary: We introduce PluGeN4Faces, a plugin to StyleGAN, which explicitly disentangles face attributes from a person's identity. Our experiments demonstrate that the modifications of face attributes performed by PluGeN4Faces are significantly less invasive on the remaining characteristics of the image than in the existing state-of-the-art models.
Score: 15.753131748318335
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Conditional GANs are frequently used for manipulating the attributes of face images, such as expression, hairstyle, pose, or age. Even though the state-of-the-art models successfully modify the requested attributes, they simultaneously modify other important characteristics of the image, such as a person's identity. In this paper, we focus on solving this problem by introducing PluGeN4Faces, a plugin to StyleGAN, which explicitly disentangles face attributes from a person's identity. Our key idea is to perform training on images retrieved from movie frames, where a given person appears in various poses and with different attributes. By applying a type of contrastive loss, we encourage the model to group images of the same person in similar regions of latent space. Our experiments demonstrate that the modifications of face attributes performed by PluGeN4Faces are significantly less invasive on the remaining characteristics of the image than in the existing state-of-the-art models.

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