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https://github.com/prinsphield/genegan
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
https://github.com/prinsphield/genegan
attributes-subspace celeba-dataset generative-adversarial-network image-interpolation image-manipulation
Last synced: 8 days ago
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GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
- Host: GitHub
- URL: https://github.com/prinsphield/genegan
- Owner: Prinsphield
- License: gpl-3.0
- Created: 2017-05-04T12:50:51.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-04-26T08:13:26.000Z (over 6 years ago)
- Last Synced: 2023-10-20T22:39:17.980Z (about 1 year ago)
- Topics: attributes-subspace, celeba-dataset, generative-adversarial-network, image-interpolation, image-manipulation
- Language: Python
- Homepage: https://arxiv.org/abs/1705.04932v1
- Size: 844 KB
- Stars: 144
- Watchers: 10
- Forks: 34
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
By Shuchang Zhou, Taihong Xiao, Yi Yang, Dieqiao Feng, Qinyao He, Weiran He
If you use this code for your research, please cite our paper:
```
@inproceedings{DBLP:conf/bmvc/ZhouXYFHH17,
author = {Shuchang Zhou and
Taihong Xiao and
Yi Yang and
Dieqiao Feng and
Qinyao He and
Weiran He},
title = {GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data},
booktitle = {Proceedings of the British Machine Vision Conference (BMVC)},
year = {2017},
url = {http://arxiv.org/abs/1705.04932},
timestamp = {http://dblp.uni-trier.de/rec/bib/journals/corr/ZhouXYFHH17},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
```
We have two following papers, [DNA-GAN](https://github.com/Prinsphield/DNA-GAN) and [ELEGANT](https://github.com/Prinsphield/ELEGANT), that generalize the method into multiple attributes case. It is worth mentioning that [ELEGANT](https://github.com/Prinsphield/ELEGANT) can transfer multiple face attributes on high resolution images. Please pay attention to our new methods!
### Introduction
This is the official source code for the paper [GeneGAN: Learning Object Transfiguration
and Attribute Subspace from Unpaired Data](https://arxiv.org/abs/1705.04932v1). All the experiments are initially done in
our proprietary deep learning framework. For convenience, we reproduce the results using TensorFlow.
GeneGAN is a deterministic conditional generative model that can learn to disentangle the object
features from other factors in feature space from weak supervised 0/1 labeling of training data.
It allows fine-grained control of generated images on one certain attribute in a continous way.
### Requirement
- Python 3.5
- TensorFlow 1.0
- Opencv 3.2
### Training GeneGAN on celebA dataset
0. Download [celebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset and unzip it into
`datasets` directory. There are various source providers for CelebA datasets. To ensure that the
size of downloaded images is correct, please run `identify datasets/celebA/data/000001.jpg`. The
size should be 409 x 687 if you are using the same dataset. Besides, please ensure that you have
the following directory tree structure.
```
├── datasets
│ └── celebA
│ ├── data
│ ├── list_attr_celeba.txt
│ └── list_landmarks_celeba.txt
```
1. Run `python preprocess.py`. It will take several miniutes to preprocess all face images.
A new directory `datasets/celebA/align_5p` will be created.
2. Run `python train.py -a Bangs -g 0` to train GeneGAN on the attribute `Bangs`.
You can train GeneGAN on other attributes as well. All available attribute names are
listed in the `list_attr_celeba.txt` file.
3. Run `tensorboard --logdir='./' --port 6006` to watch your training process.
### Testing
We provide three kinds of mode for test. Run `python test.py -h` for detailed help.
The following example is running on our GeneGAN model trained on the attribute
`Bangs`. Have fun!
#### 1. Swapping of Attributes
You can easily add the bangs of one person to another person without bangs by running
python test.py -m swap -i datasets/celebA/align_5p/182929.jpg -t datasets/celebA/align_5p/022344.jpg
Swap Attribute
#### 2. Linear Interpolation of Image Attributes
Besides, we can control to which extent the bangs style is added to your input image
through linear interpolation of image attribute. Run the following code.
python test.py -m interpolation -i datasets/celebA/align_5p/182929.jpg -t datasets/celebA/align_5p/035460.jpg -n 5
Linear Interpolation
#### 3. Matrix Interpolation in Attribute Subspace
We can do something cooler. Given four images with bangs attributes at hand,
we can observe the gradual change process of our input images with a mixing of
difference bangs style.
python test.py -m matrix -i datasets/celebA/align_5p/182929.jpg --targets datasets/celebA/align_5p/035460.jpg datasets/celebA/align_5p/035451.jpg datasets/celebA/align_5p/035463.jpg datasets/celebA/align_5p/035474.jpg -s 5 5
Matrix Interpolation