diff --git a/README.md b/README.md index def521c..049f3cd 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,18 @@ # GFPGAN (CVPR 2021) -[English](README.md) **|** [简体中文](README_CN.md) - -[[Paper]](https://arxiv.org/abs/2101.04061) **|** [[Project Page]](https://xinntao.github.io/projects/gfpgan) +[**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) GFPGAN is a blind face restoration algorithm towards real-world face images. ### GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior -[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) + +[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)
Applied Research Center (ARC), Tencent PCG +#### Abstract + +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. + #### BibTeX @InProceedings{wang2021gfpgan, diff --git a/README_CN.md b/README_CN.md index def521c..049f3cd 100644 --- a/README_CN.md +++ b/README_CN.md @@ -1,15 +1,18 @@ # GFPGAN (CVPR 2021) -[English](README.md) **|** [简体中文](README_CN.md) - -[[Paper]](https://arxiv.org/abs/2101.04061) **|** [[Project Page]](https://xinntao.github.io/projects/gfpgan) +[**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) GFPGAN is a blind face restoration algorithm towards real-world face images. ### GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior -[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) + +[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)
Applied Research Center (ARC), Tencent PCG +#### Abstract + +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. + #### BibTeX @InProceedings{wang2021gfpgan,