Semantic and Geometric Unfolding of StyleGAN Latent Space release_i4ryd4hokrfj5kfkkfewiebfhq

by Mustafa Shukor, Xu Yao, Bharath Bhushan Damodaran, Pierre Hellier

Released as a article .

2021  

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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Type  article
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Date   2021-07-09
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Language   en ?
arXiv  2107.04481v1
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