Latent Space Editing

Latent space editing focuses on manipulating the internal representations (latent spaces) of generative models, like GANs and diffusion models, to achieve controlled image manipulation. Current research emphasizes developing methods for more intuitive and disentangled latent spaces, often employing transformer-based architectures or contrastive learning to achieve finer control and better semantic understanding. This allows for high-quality image editing tasks such as reference-based edits, text-guided modifications, and even longitudinal medical image synthesis, impacting fields ranging from artistic image manipulation to medical image analysis. The ability to precisely control image generation and editing through latent space manipulation is driving significant advancements in various applications.