Human Body Restoration with One-Step Diffusion Model and A New Benchmark
| Title: | Human Body Restoration with One-Step Diffusion Model and A New Benchmark |
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| 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 |
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