tencentarc / vqfr

Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder

Demo API Examples Versions (f9085ea5)

Examples

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Run time and cost

Predictions run on Nvidia T4 GPU hardware. Predictions typically complete within 6 seconds.

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:

Taming-transformers

GFPGAN

DistSup

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 yuchaogu9710@gmail.com.