SC Diff

SC-Diff, or more broadly, diffusion models, are a class of generative models increasingly used to solve diverse inverse problems across various scientific domains. Current research focuses on adapting diffusion model architectures, such as conditional and latent diffusion models, to tasks like 3D shape completion, image-based motion prediction, and hyperspectral image super-resolution, often incorporating techniques like autoencoders and transformers for improved performance. These advancements demonstrate the power of diffusion models for generating high-quality outputs from noisy or incomplete data, impacting fields ranging from computer vision and robotics to climate modeling and medical imaging. The ability to handle uncertainty and generate realistic results makes diffusion models a promising tool for numerous applications.