Gender	Bangs	Body Side	Pose (Yaw)
Lighting	Smile	Face Shape	Lipstick Color

Painting Style	Pose (Yaw)	Pose (Pitch)	Zoom & Rotate
Flush & Eye Color	Mouth Shape	Hair Color	Hue (Orange-Blue)

more unsupervisedly learned dimensions

EigenGAN

EigenGAN: Layer-Wise Eigen-Learning for GANs \ Zhenliang He^1,2, Meina Kan^1,2, Shiguang Shan^1,2,3 \ ¹Key Lab of Intelligent Information Processing, Institute of Computing Technology, CAS, China \ ²University of Chinese Academy of Sciences, China \ ³Peng Cheng Laboratory, China

Schema

Manifold Perspective

Usage

• Environment

  • Python 3.6

  • TensorFlow 1.15

  • OpenCV, scikit-image, tqdm, oyaml

  • we recommend Anaconda or Miniconda, then you can create the environment with commands below

    ```console conda create -n EigenGAN python=3.6

    source activate EigenGAN

    conda install opencv scikit-image tqdm tensorflow-gpu=1.15

    conda install -c conda-forge oyaml ```

  • NOTICE: if you create a new conda environment, remember to activate it before any other command

    console source activate EigenGAN

• Data Preparation

  • CelebA-unaligned (10.2GB, higher quality than the aligned data)

    • download the dataset

      • img_celeba.7z (move to ./data/img_celeba/img_celeba.7z): Google Drive or Baidu Netdisk (password rp0s)

      • annotations.zip (move to ./data/img_celeba/annotations.zip): Google Drive

    • unzip and process the data

      ```console 7z x ./data/img_celeba/img_celeba.7z/img_celeba.7z.001 -o./data/img_celeba/

      unzip ./data/img_celeba/annotations.zip -d ./data/img_celeba/

      python ./scripts/align.py ```

  • Anime

    • download the dataset

      ```console mkdir -p ./data/anime

      rsync --verbose --recursive rsync://176.9.41.242:873/biggan/portraits/ ./data/anime/original_imgs ```

    • process the data

      console python ./scripts/remove_black_edge.py

• Run (support multi-GPU)

  • training on CelebA

    console CUDA_VISIBLE_DEVICES=0,1 \ python train.py \ --img_dir ./data/img_celeba/aligned/align_size(572,572)_move(0.250,0.000)_face_factor(0.450)_jpg/data \ --experiment_name CelebA

  • training on Anime

    console CUDA_VISIBLE_DEVICES=0,1 \ python train.py \ --img_dir ./data/anime/remove_black_edge_imgs \ --experiment_name Anime

  • testing

    console CUDA_VISIBLE_DEVICES=0 \ python test_traversal_all_dims.py \ --experiment_name CelebA

  • loss visualization

    console CUDA_VISIBLE_DEVICES='' \ tensorboard \ --logdir ./output/CelebA/summaries \ --port 6006

• Using Trained Weights

  • trained weights (move to ./output/*.zip)

    • CelebA.zip

    • Anime.zip

  • unzip the file (CelebA.zip for example)

    console unzip ./output/CelebA.zip -d ./output/

  • testing (see above)

Citation

If you find EigenGAN useful in your research works, please consider citing:

@inproceedings{he2021eigengan,
  title={EigenGAN: Layer-Wise Eigen-Learning for GANs},
  author={He, Zhenliang and Kan, Meina and Shan, Shiguang},
  booktitle={IEEE International Conference on Computer Vision (ICCV)},
  year={2021}
}