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63 lines
3.1 KiB
Markdown
63 lines
3.1 KiB
Markdown
# GFPGAN (CVPR 2021)
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[**Paper**](https://arxiv.org/abs/2101.04061) **|** [**Project Page**](https://xinntao.github.io/projects/gfpgan)    [English](README.md) **|** [简体中文](README_CN.md)
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GFPGAN is a blind face restoration algorithm towards real-world face images.
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### :book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior
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> [[Paper](https://arxiv.org/abs/2101.04061)]   [[Project Page](https://xinntao.github.io/projects/gfpgan)]   [[Demo]()] <br>
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> [Xintao Wang](https://xinntao.github.io/), [Yu Li](https://yu-li.github.io/), [Honglun Zhang](https://scholar.google.com/citations?hl=en&user=KjQLROoAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en) <br>
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> Applied Research Center (ARC), Tencent PCG
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#### Abstract
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Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.
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#### BibTeX
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@InProceedings{wang2021gfpgan,
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author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan},
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title = {Towards Real-World Blind Face Restoration with Generative Facial Prior},
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booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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year = {2021}
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}
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<p align="center">
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<img src="https://xinntao.github.io/projects/GFPGAN_src/gfpgan_teaser.jpg">
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</p>
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---
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## :wrench: Dependencies and Installation
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- Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
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- [PyTorch >= 1.7](https://pytorch.org/)
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- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
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### Installation
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1. Clone repo
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```bash
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git clone https://github.com/xinntao/GFPGAN.git
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```
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1. Install dependent packages
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```bash
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cd GFPGAN
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pip install -r requirements.txt
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```
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## :zap: Quick Inference
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> python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs
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## :scroll: License and Acknowledgement
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GFPGAN is realeased under Apache License Version 2.0.
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## :e-mail: Contact
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If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
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