GAN Inversion

GAN inversion aims to map real-world images into the latent space of a pre-trained Generative Adversarial Network (GAN), enabling image manipulation through latent code editing. Current research focuses on improving the fidelity of image reconstruction while maintaining the editability of the latent codes, often employing techniques like dual encoders, diffusion models, and recurrent networks within StyleGAN and other 3D-aware GAN architectures. This field is significant because it allows for high-quality image editing and generation tasks across diverse applications, including face manipulation, 3D reconstruction, and anomaly detection, by leveraging the powerful generative capabilities of GANs.