StyleGAN Inversion

StyleGAN inversion aims to map real-world images into the latent space of a pre-trained StyleGAN generator, enabling high-fidelity image editing by manipulating latent codes. Current research focuses on improving the trade-off between reconstruction quality and editability, exploring different latent spaces (W, W+, F) and employing techniques like hypernetworks, contrastive learning, and multi-phase architectures to achieve both accurate image representation and effective manipulation. These advancements are significant for various applications, including image restoration, inpainting, and text-driven image editing, offering powerful tools for image manipulation and analysis.