DiffBody: Human Body Image Restoration with Generative Diffusion Prior

Human body image restoration is crucial for various applications but remains challenging due to the limitations of generative models: General image restoration methods built on generative models may generate unnatural textures, noticeable structural misalignments, and significant loss of fine details. To address these shortcomings, we present DiffBody, a novel human body-aware diffusion model that incorporates domain-specific knowledge to significantly enhance restoration quality. Our approach adopts a two-stage framework: (1) a multi-branch joint diffusion model generates preliminary priors, including normal and depth maps supported by a robust reconstruction pre-processing step; (2) a restoration stage refines the output using a body-prior ControlNet and a color adapter, ensuring structural accuracy and color consistency. Extensive quantitative evaluations, qualitative evaluations, and user studies validate the superior performance of DiffBody in producing perceptually high-quality human body restoration results. Code is available at https://github.com/yimingz1218/DiffBody.