• AnonyGAN
• Installation
• Dataset Preparation
• Generating Images Using Pretrained Model
• Train and Test New Models
• Evaluation
• Acknowledgments
• Citation
• Contributions

| Paper |
Graph-based Generative Face Anonymisation with Pose Preservation
Nicola Dall'Asen¹², Yiming Wang³, Hao Tang⁴, Luca Zanella³, Elisa Ricci²³.
¹University of Pisa, Italy, ²University of Trento, Italy, ³Fondazione Bruno Kessler, Italy, ⁴ETH Zürich, Switzerland.
In ICIAP 2021.
The repository offers the official implementation of our paper in PyTorch.

Clone this repo.

git clone [email protected]:Fodark/anonygan.git
cd anonygan/

Needed libraries are provided in the requirements.txt file.

pip install -r requirements.txt should suffice.

• Download aligned CelebA here
• Extract aligned version
• Compute landmarks and mask with the code provided in preparation (modify paths accordingly)

• Download pretrained model here
• Place it in ckpts/anonygan.ckpt
• Preprocess your images with the files in preparation
• Prepare a .csv files with columns [from, to] with condition and source images names
• Run test.sh modifying paths accordingly

• Same as using the pretrained model, for training modify train.sh accordingly

evaluation/automatic_evaluation is the entry point, modify paths accordingly

Graph reasoning inspired by BiGraphGAN

If you use this code for your research, please consider giving a star and citing our paper!

@inproceedings{dallasen2021anonygan,
  title={Graph-based Generative Face Anonymisation with Pose Preservation},
  author={Dall'Asen, Nicola and Wang, Yiming and Tang, Hao and Zanella, Luca and Ricci, Elisa},
  booktitle={International Conference on Image analysis and Processing},
  year={2021}
}

If you have any questions/comments/bug reports, feel free to open a github issue or pull a request or e-mail to the author Nicola Dall'Asen ([email protected]).