Semantic and Geometric Unfolding of StyleGAN Latent Space
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by
Mustafa Shukor, Xu Yao, Bharath Bhushan Damodaran, Pierre Hellier
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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