Semantic and Geometric Unfolding of StyleGAN Latent Space

• URL: http://arxiv.org/abs/2107.04481v1
• Date: Fri, 9 Jul 2021 15:12:55 GMT
• Title: Semantic and Geometric Unfolding of StyleGAN Latent Space
• Authors: Mustafa Shukor, Xu Yao, Bharath Bhushan Damodaran, Pierre Hellier
• Abstract summary: Generative adversarial networks (GANs) have proven to be surprisingly efficient for image editing. In this paper, we identify two geometric limitations of such latent space. We propose a new method to learn a proxy latent representation using normalizing flows to remedy these limitations.
• Score: 2.7910505923792646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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