Bibliographic Details
| Title: |
Human Body Restoration with One-Step Diffusion Model and A New Benchmark |
| Authors: |
Gong, Jue, Wang, Jingkai, Chen, Zheng, Liu, Xing, Gu, Hong, Zhang, Yulun, Yang, Xiaokang |
| Publication Year: |
2025 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computer Vision and Pattern Recognition |
| More Details: |
Human body restoration, as a specific application of image restoration, is widely applied in practice and plays a vital role across diverse fields. However, thorough research remains difficult, particularly due to the lack of benchmark datasets. In this study, we propose a high-quality dataset automated cropping and filtering (HQ-ACF) pipeline. This pipeline leverages existing object detection datasets and other unlabeled images to automatically crop and filter high-quality human images. Using this pipeline, we constructed a person-based restoration with sophisticated objects and natural activities (\emph{PERSONA}) dataset, which includes training, validation, and test sets. The dataset significantly surpasses other human-related datasets in both quality and content richness. Finally, we propose \emph{OSDHuman}, a novel one-step diffusion model for human body restoration. Specifically, we propose a high-fidelity image embedder (HFIE) as the prompt generator to better guide the model with low-quality human image information, effectively avoiding misleading prompts. Experimental results show that OSDHuman outperforms existing methods in both visual quality and quantitative metrics. The dataset and code will at https://github.com/gobunu/OSDHuman.
Comment: 8 pages, 9 figures. The code and model will be available at https://github.com/gobunu/OSDHuman |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2502.01411 |
| Accession Number: |
edsarx.2502.01411 |
| Database: |
arXiv |
