Designing an Encoder for StyleGAN Image Manipulation

Understand

Recently, there has been a surge of diverse methods for performing image editing by employing pre-trained unconditional generators.

• Applying these methods on real images, however, remains a challenge, as it necessarily requires the inversion of the images into their latent space.
• To successfully invert a real image, one needs to find a latent code that reconstructs the input image accurately, and more importantly, allows for its meaningful manipulation.
• In this paper, we carefully study the latent space of StyleGAN, the state-of-the-art unconditional generator.