Beyond Alignment: Blind Video Face Restoration via Parsing-Guided Temporal-Coherent Transformer

Kepeng Xu  Li Xu  Gang He et al

Xidian University,Southwest University of Science and Technology 

IJCAI 2024

🔥 For more results, visit our project page,gif page 🔥
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Update

• 2024.08: We released the initial version of the inference code and models. Stay tuned for continuous updates!
• 2024.04: This repo is created!

Getting Started

Dependencies and Installation

required packages in requirements

# git clone this repository
git clone https://github.com/kepengxu/PGTFormer
cd PGTFormer

# create new anaconda env
conda create -n pgtformer python=3.8 -y
conda activate pgtformer

# install python dependencies
conda install -c conda-forge dlib
conda install -c conda-forge ffmpeg

Quick Inference

Download Pre-trained Models

All pretrained models can also be automatically downloaded during the first inference. You can also download our pretrained models from Google DriveBaiduYun to the weights folder.

Prepare Testing Data

We provide a example in assets/inputdemovideo.mp4. If you would like to test your own face videos, place them in the same folder.

Inference

[Note] 🚀🚀🚀 Our method does not require pre-alignment to standard face poses and has better consistency. The results will be saved in the results folder.

🧑🏻 Video Face Restoration It is highly recommended to use ffmpeg to enhance the encoded video online, which can maintain the restored quality as much as possible.

# Just Run This 
# Input Video Width and Height == 512
python inference.py --input_video=assets/inputdemovideo.mp4 --output_video=exp/output_demo.mp4

🚀🚀🚀Awesome Video Face Restoration Method Can be Found There!🚀🚀🚀

Awesome Video Face Restoration

🔥 News

• 2024.04: 🎉 This paper is accepted by IJCAI 2024

• Beyond Alignment: Blind Video Face Restoration via Parsing-Guided Temporal-Coherent Transformer (IJCAI 2024)

Results report in VFHQ implementation.

Metrics	Bicubic	EDVRM	BasicVSR	EDVRM-GAN	BasicVSR-GAN	DFDNet	GFP-GAN	GPEN
PSNR	26.842	29.457 (Red)	29.472 (Red)	26.682	25.813	25.178	25.978	26.672
SSIM	0.7909	0.8428 (Red)	0.8430 (Red)	0.7638	0.741	0.7560	0.7723	0.7768
LPIPS	0.4098	0.3288	0.3309	0.3076 (Red)	0.3214 (Red)	0.4008	0.3446	0.3607

Results reported in our implementation.

Method	PSNR	SSIM	LPIPS	Deg	LMD	TLME	MSRL		PSNR	SSIM	LPIPS	Deg	LMD	TLME	MSRL
BasicVSR++	26.70	0.7906	0.3086	38.31	2.89	6.97	24.15		26.17	0.7482	0.3594	36.14	2.39	7.09	23.91
HiFaceGAN	28.45	0.8297	0.2924	34.02	2.25	5.73	25.81		27.41	0.7926	0.3167	32.74	1.99	5.59	24.99
GFP-GAN	27.77	0.8252	0.2893	31.70	2.40	6.11	25.68		26.27	0.7864	0.3167	30.14	2.13	6.17	24.69
VQFR	25.59	0.7788	0.3003	37.83	2.99	7.41	23.60		25.33	0.7459	0.3134	33.27	2.40	7.05	23.04
Codeformer	28.71	0.8226	0.2460	28.11	1.97	5.82	26.32		27.88	0.8018	0.2738	26.55	1.74	5.60	25.54
Ours	30.74	0.8668	0.2095	24.41	1.63	4.20	28.16		29.66	0.8408	0.2230	23.09	1.35	4.09	27.33

Acknowledgement

This project is based on BasicSR. Some codes are brought from Codeformer. We also adopt the VFHQ dataset to train network.

Citation

If you find our repo useful for your research, please consider citing our paper:

@article{xu2024beyond,
title={Beyond Alignment: Blind Video Face Restoration via Parsing-Guided Temporal-Coherent Transformer},
author={Xu, Kepeng and Xu, Li and He, Gang and Yu, Wenxin and Li, Yunsong},
journal={IJCAI 2024},
year={2024}
}

License and Acknowledgement

This project is open sourced under (https://github.com/kepengxu/PGTFormer/blob/main/LICENSE). Redistribution and use should follow this license. The code framework is mainly modified from CodeFormer. The page is modified from KEEP

Contact

If you have any question, please feel free to contact us via [email protected].