GAN Latent Space

GAN latent space research focuses on understanding and manipulating the internal representations of generative adversarial networks (GANs) to achieve greater control over image generation and editing. Current efforts concentrate on discovering interpretable directions within these latent spaces, often using techniques like optimization-based methods and hypernetworks, to enable fine-grained control over specific image attributes (e.g., facial expressions, object placement) across various GAN architectures, including StyleGAN and diffusion models. This work is significant because it allows for more precise and creative image manipulation, with applications ranging from realistic face reenactment to high-quality image editing and the generation of diverse synthetic datasets for training other models.