This is the implementation of Qingxing Cao et al.'s CVPR-17 work Attention-Aware Face Hallucination via Deep Reinforcement Learning.

Please cite Attention-FH in your publications if it helps your research:

@article{Cao2017Attention,
  title={Attention-Aware Face Hallucination via Deep Reinforcement Learning},
  author={Cao, Qingxing and Lin, Liang and Shi, Yukai and Liang, Xiaodan and Li, Guanbin},
  pages={1656-1664},
  year={2017},
}

• Computer with Linux or OSX
• Torch-7
• For training, an NVIDIA GPU is strongly recommended for speed. CPU is supported but training is very slow.
• Cuda 8.0

• Install Torch: http://torch.ch/docs/getting-started.html
• Install Element-Research/rnn: https://github.com/Element-Research/rnn

• After all dependencies installed, you should put your own training and testing files as:

../lfw_funneled_dev_128/train/Aaron_Eckhart/Aaron_Eckhart_0001.jpg
../lfw_funneled_dev_128/test/Aaron_Guiel/Aaron_Guiel_0001.jpg

Our data has processed with 2 points aligned and crop the centric part. We suggest you to use CFSS or other face alignment methods to pre-process your data. Then you can train the model by using the following command:

python RNN_main.lua

If you have any questions or suggestions of this work, please feel free to contact the authors by sending email to caoqxATmail2.sysu.edu.cn or shiyk3ATmail2.sysu.edu.cn.

This work was benifited a lot from DCGAN and Recurrent Models of Visual Attention, thank for their great job.