StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN

Understand

Recently, StyleGAN has enabled various image manipulation and editing tasks thanks to the high-quality generation and the disentangled latent space.

• However, additional architectures or task-specific training paradigms are usually required for different tasks.
• In this work, we take a deeper look at the spatial properties of StyleGAN.
• We show that with a pretrained StyleGAN along with some operations, without any additional architecture, we can perform comparably to the state-of-the-art methods on various tasks, including image blending, panorama generation, generation from a single image, controllable and local multimodal image to image translation, and attributes transfer.