HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing

Understand

The inversion of real images into StyleGAN's latent space is a well-studied problem.

• Nevertheless, applying existing approaches to real-world scenarios remains an open challenge, due to an inherent trade-off between reconstruction and editability: latent space regions which can accurately represent real images typically suffer from degraded semantic control.
• Recent work proposes to mitigate this trade-off by fine-tuning the generator to add the target image to well-behaved, editable regions of the latent space.
• While promising, this fine-tuning scheme is impractical for prevalent use as it requires a lengthy training phase for each new image.