Generative adversarial networks (GANs) have proven to be surprisingly efficient for image editing by inverting and manipulating the latent code corresponding to a natural image. This property emerges from the disentangled nature of the latent space. In this paper, we identify two geometric limitations of such latent space: (a) euclidean distances differ from image perceptual distance, and (b) disentanglement is not optimal and facial attribute separation using linear model is a limiting hypothesis. We thus propose a new method to learn a proxy latent representation using normalizing flows to remedy these limitations, and show that this leads to a more efficient space for face image editing.
@article{arxiv.2107.04481,
title = {Semantic and Geometric Unfolding of StyleGAN Latent Space},
author = {Mustafa Shukor and Xu Yao and Bharath Bhushan Damodaran and Pierre Hellier},
journal= {arXiv preprint arXiv:2107.04481},
year = {2021}
}