Awesome

Gender	Bangs	Body Side	Pose (Yaw)
<img src="pics/attributes_gif_face/celeba_3-1_gender.gif" width="100%">	<img src="pics/attributes_gif_face/celeba_3-4_bangs.gif" width="100%">	<img src="pics/attributes_gif_face/celeba_3-6_body_side.gif" width="100%">	<img src="pics/attributes_gif_face/celeba_4-1_pose.gif" width="100%">
Lighting	Smile	Face Shape	Lipstick Color
<img src="pics/attributes_gif_face/celeba_4-4_lighting.gif" width="100%">	<img src="pics/attributes_gif_face/celeba_4-5_smiling.gif" width="100%">	<img src="pics/attributes_gif_face/celeba_4-6_face_shape.gif" width="100%">	<img src="pics/attributes_gif_face/celeba_5-6_lipstick_color.gif" width="100%">

Painting Style	Pose (Yaw)	Pose (Pitch)	Zoom & Rotate
<img src="pics/attributes_gif_anime/2-5_painting_style.gif" width="100%">	<img src="pics/attributes_gif_anime/3-2_pose.gif" width="100%">	<img src="pics/attributes_gif_anime/3-5_pose.gif" width="100%">	<img src="pics/attributes_gif_anime/3-3_zoom+rotate.gif" width="100%">
Flush & Eye Color	Mouth Shape	Hair Color	Hue (Orange-Blue)
<img src="pics/attributes_gif_anime/4-1_flush+eye_color.gif" width="100%">	<img src="pics/attributes_gif_anime/4-2_mouth_shape.gif" width="100%">	<img src="pics/attributes_gif_anime/5-1_hair_color.gif" width="100%">	<img src="pics/attributes_gif_anime/6-1_hue.gif" width="100%">

More Unsupervisedly Learned Dimensions

EigenGAN (ICCV 2021) video

TensorFlow implementation of EigenGAN: Layer-Wise Eigen-Learning for GANs
Schema
 <img src="./pics/schema.svg" width="100%" />
Manifold Perspective
 <img src="./pics/manifold.svg" width="100%" />

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

rsync --verbose --recursive rsync://176.9.41.242:873/biggan/portraits/ ./data/anime/original_imgs
```
  - process the data
```
python ./scripts/remove_black_edge.py
```

Run (support multi-GPU)

training on CelebA

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

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

testing

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

loss visualization

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)
```
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={International Conference on Computer Vision (ICCV)},
  year={2021}
}

Awesome

More Unsupervisedly Learned Dimensions

<p align="center"> EigenGAN (ICCV 2021) <br> <sub> video </sub> </br> </p>

Usage

Citation