Readme
This is the cog implementation from https://github.com/TencentARC/VQFR
VQFR (ECCV 2022 Oral)
This paper aims at investigating the
potential and limitation of Vector-Quantized (VQ) dictionary
for blind face restoration.
We propose a new framework VQFR – incoporating the
Vector-Quantized Dictionary
and the
Parallel Decoder
.
Compare with previous arts, VQFR produces more realistic facial details and keep the comparable fidelity.
VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder
Yuchao Gu , Xintao Wang , Liangbin Xie , Chao Dong , Gen Li, Ying Shan , Ming-Ming Cheng
Nankai University; Tencent ARC Lab; Tencent Online Video; Shanghai AI Laboratory;
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
License
VQFR is released under Apache License Version 2.0.
Acknowledgement
Thanks to the following open-source projects:
Citation
@inproceedings{gu2022vqfr,
title={VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder},
author={Gu, Yuchao and Wang, Xintao and Xie, Liangbin and Dong, Chao and Li, Gen and Shan, Ying and Cheng, Ming-Ming},
year={2022},
booktitle={ECCV}
}
Contact
If you have any question, please email
[email protected]
.


