Semantic Unfolding of StyleGAN Latent Space

• URL: http://arxiv.org/abs/2206.14892v1
• Date: Wed, 29 Jun 2022 20:22:10 GMT
• Title: Semantic Unfolding of StyleGAN Latent Space
• Authors: Mustafa Shukor, Xu Yao, Bharath Bushan Damodaran, Pierre Hellier
• Abstract summary: Generative adversarial networks (GANs) have proven to be surprisingly efficient for image editing by inverting and manipulating the latent code corresponding to an input real image. This editing property emerges from the disentangled nature of the latent space. In this paper, we identify that the facial attribute disentanglement is not optimal, thus facial editing relying on linear attribute separation is flawed.
• Score: 0.7646713951724012
• 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 an input real image. This editing property emerges from the disentangled nature of the latent space. In this paper, we identify that the facial attribute disentanglement is not optimal, thus facial editing relying on linear attribute separation is flawed. We thus propose to improve semantic disentanglement with supervision. Our method consists in learning a proxy latent representation using normalizing flows, and we show that this leads to a more efficient space for face image editing.

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