GANalyzer

https://aliprf.github.io/GANalyzer/

! plaese STAR the repo if you like it.

GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesis

Link to the pre-print:

https://arxiv.org/abs/2302.00908

Link to the paper:

Link to the paper-with-codes:

https://paperswithcode.com/paper/ganalyzer-analysis-and-manipulation-of-gans

Please cite this work as:
@misc{https://doi.org/10.48550/arxiv.2302.00908,
  doi = {10.48550/ARXIV.2302.00908},
  url = {https://arxiv.org/abs/2302.00908},
  author = {Fard, Ali Pourramezan and Mahoor, Mohammad H. and Lamer, Sarah Ariel and Sweeny, Timothy},
  title = {GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesis},
  publisher = {arXiv},
  year = {2023},
}

Qualitative Results Evaluation:

The following are some examples from the paper, showing the performance of GANalyzer for both facial attribute editing, and intensity-based modification.

Facial Attribute Editing:

Intensity-Based Attribute Editing:

Creating Environment

$ conda env create -f environment.yml
$ conda activate ganalyzer
$ pip install -r requirements.txt

Facial Attribute Editing

In the first step, we need to create the modification task using test.py->create_single_FAE_task() function. The following tasks are generated as default. You can modify each task according to explaination in the paper.

  fae_tasks = [{'tn': 'ANGRY', 'alpha': 2.5, 'beta': 1.0},
                 {'tn': 'BLACK', 'alpha': 2.0, 'beta': 1.0},
                 {'tn': 'FEMALE', 'alpha': 3.5, 'beta': 1.0},
                 {'tn': 'MALE', 'alpha': 3.0, 'beta': 1.0},
                 {'tn': 'OLD', 'alpha': 4.0, 'beta': 1.0},
                 {'tn': 'YOUNG', 'alpha': 4.0, 'beta': 1.0},
                 ]

After creating the tasks, you can pass each task to test.py->modify_noise() function and modify the arbitrary input latent vectors.

Feature-based Synthesis

In the first step, we need to create the modification task using test.py->create_single_FB_task() function. The following tasks are generated as default. You can modify each task according to explaination in the paper.

   fb_tasks = [{'tn': 'ANGRY', 'alpha': 1.0, 'beta': 0.25},
                {'tn': 'BLACK', 'alpha': 1.0, 'beta': 0.25},
                {'tn': 'FEMALE', 'alpha': 1.0, 'beta': 0.25},
                {'tn': 'MALE', 'alpha': 1.0, 'beta': 0.25},
                {'tn': 'OLD', 'alpha': 1.0, 'beta': 0.25},
                {'tn': 'YOUNG', 'alpha': 1.0, 'beta': 0.25},
                ]

After creating the tasks, you can pass each task to test.py->modify_noise() function and modify the arbitrary input latent vectors.

Image Generation

GANalyzer is a framework designed for latent space modification, and no face synthesis is part of it. We use StylGAN3 for image synthesis. Hence, the image generation function which is located in 'test.py->generate_images()' is provided by stylegan3 repo in the following address:

https://github.com/NVlabs/stylegan3

! IF YOU USE THIS FUNCTION, YOU NEED TO FOLLOW StyleGAN3 Licence:
https://github.com/NVlabs/stylegan3/blob/main/LICENSE.txt

you need to download ffhq.pkl file following the documentation provided by stylegan3 repo.

Reference:

Please cite this work as:
@misc{https://doi.org/10.48550/arxiv.2302.00908,
  doi = {10.48550/ARXIV.2302.00908},
  url = {https://arxiv.org/abs/2302.00908},
  author = {Fard, Ali Pourramezan and Mahoor, Mohammad H. and Lamer, Sarah Ariel and Sweeny, Timothy},
  title = {GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesis},
  publisher = {arXiv},
  year = {2023},
}

@@plaese STAR the repo if you like it.@@