Latent Mapper

Latent mapping techniques aim to create efficient and flexible representations of complex data, such as images and 3D shapes, by encoding them into lower-dimensional latent spaces. Current research focuses on developing novel architectures, including variational autoencoders (VAEs) and diffusion models, to improve the quality, speed, and generalizability of these mappings, often leveraging pre-trained large models for enhanced performance. These advancements enable applications ranging from high-resolution image generation and 3D reconstruction to sophisticated image editing and the creation of realistic 3D assets, impacting fields like computer vision, graphics, and robotics.