yangxy / gpen

Blind Face Restoration in the Wild

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Input

Output

Run time and cost

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 135 seconds. The predict time for this model varies significantly based on the inputs.

Readme

GAN Prior Embedded Network for Blind Face Restoration in the Wild

Paper | Supplementary | Demo

Tao Yang, Peiran Ren, Xuansong Xie, Lei Zhang DAMO Academy, Alibaba Group, Hangzhou, China Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

Main idea

Citation

If our work is useful for your research, please consider citing:

@inproceedings{Yang2021GPEN,
    title={GAN Prior Embedded Network for Blind Face Restoration in the Wild},
    author={Tao Yang, Peiran Ren, Xuansong Xie, and Lei Zhang},
    booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2021}
}

License

© Alibaba, 2021. For academic and non-commercial use only.

Acknowledgments

We borrow some codes from Pytorch_Retinaface and stylegan2-pytorch.

Contact

If you have any questions or suggestions about this paper, feel free to reach me at yangtao9009@gmail.com.