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AnonyGAN

| Paper | <br> Graph-based Generative Face Anonymisation with Pose Preservation <br> Nicola Dall'Asen<sup>12</sup>, Yiming Wang<sup>3</sup>, Hao Tang<sup>4</sup>, Luca Zanella<sup>3</sup>, Elisa Ricci<sup>23</sup>. <br><sup>1</sup>University of Pisa, Italy, <sup>2</sup>University of Trento, Italy, <sup>3</sup>Fondazione Bruno Kessler, Italy, <sup>4</sup>ETH Zürich, Switzerland.<br> In ICIAP 2021. <br> The repository offers the official implementation of our paper in PyTorch.

Installation

Clone this repo.

git clone git@github.com:Fodark/anonygan.git
cd anonygan/

Needed libraries are provided in the requirements.txt file.

pip install -r requirements.txt should suffice.

Dataset Preparation

Generating Images Using Pretrained Model

Train and Test New Models

Evaluation

evaluation/automatic_evaluation is the entry point, modify paths accordingly

Acknowledgments

Graph reasoning inspired by BiGraphGAN

Citation

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}
}

Contributions

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 (nicola.dallasen@phd.unipi.it).