Face Identity-Aware Disentanglement in StyleGAN
Adrian Suwała Bartosz Wójcik Magdalena Proszewska Jacek Tabor Przemysław Spurek Marek Śmieja
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.