Contents
- AnonyGAN
- Installation
- Dataset Preparation
- Generating Images Using Pretrained Model
- Train and Test New Models
- Evaluation
- Acknowledgments
- Citation
- Contributions
AnonyGAN
| 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.
Installation
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.
Dataset Preparation
- Download aligned CelebA here
- Extract aligned version
- Compute landmarks and mask with the code provided in
preparation
(modify paths accordingly)
Generating Images Using Pretrained Model
- 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
Train and Test New Models
- Same as using the pretrained model, for training modify
train.sh
accordingly
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 ([email protected]).