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.

人体复原技术在各种与人体相关的应用中起着至关重要的作用。本研究提出了一种新颖的方法，通过构建人体感知扩散模型，利用领域特定知识来提高性能。通过预训练的身体注意模块来引导扩散模型在前景上的聚焦，解决主体和背景之间的混合问题。同时，通过无缝地结合文本提示来改善表面纹理质量和额外的服装和配饰细节。此外，引入了一个专门针对细粒度人体部位的扩散采样器，利用本地语义信息来修复肢体扭曲。最后，我们收集了一个全面的数据集，用于评估和推进人体复原领域的发展。大量实验证实了我们的方法在定量和定性方面的优越性。