Latent Image

Latent image representation focuses on learning compact, meaningful encodings of image data, aiming to capture essential features while discarding irrelevant details. Current research emphasizes disentangling these representations to achieve finer control over image generation, manipulation, and understanding, often employing diffusion models, variational autoencoders (VAEs), and transformers. This work is significant for advancing image synthesis, compression, and analysis, with applications ranging from robotics and medical imaging to 3D modeling and forensic science. Improved latent representations are key to enhancing the robustness and efficiency of various computer vision tasks.