  Human body restoration plays a vital role in various applications related to
the human body. Despite recent advances in general image restoration using
generative models, their performance in human body restoration remains
mediocre, often resulting in foreground and background blending, over-smoothing
surface textures, missing accessories, and distorted limbs. Addressing these
challenges, we propose a novel approach by constructing a human body-aware
diffusion model that leverages domain-specific knowledge to enhance
performance. Specifically, we employ a pretrained body attention module to
guide the diffusion model's focus on the foreground, addressing issues caused
by blending between the subject and background. We also demonstrate the value
of revisiting the language modality of the diffusion model in restoration tasks
by seamlessly incorporating text prompt to improve the quality of surface
texture and additional clothing and accessories details. Additionally, we
introduce a diffusion sampler tailored for fine-grained human body parts,
utilizing local semantic information to rectify limb distortions. Lastly, we
collect a comprehensive dataset for benchmarking and advancing the field of
human body restoration. Extensive experimental validation showcases the
superiority of our approach, both quantitatively and qualitatively, over
existing methods.
