Diffusion Prior

Diffusion priors leverage the powerful generative capabilities of pre-trained diffusion models to enhance various image and 3D reconstruction tasks. Current research focuses on integrating these priors into diverse applications, including image restoration (e.g., super-resolution, inpainting, colorization), 3D scene reconstruction (e.g., NeRFs, Gaussian splatting), and inverse problems (e.g., blind deblurring, material recovery). This approach improves the quality and efficiency of these tasks by providing strong, data-driven priors that guide the reconstruction process, often surpassing methods relying solely on traditional optimization or supervised learning. The resulting advancements have significant implications for computer vision, medical imaging, and other fields requiring high-quality image and 3D model generation from limited or noisy data.