GeneGAN.zip_92gan.com_genegan_genegan是什么_人脸生成_属性人脸生成

# 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} } ``` ### Introduction This is the official source code for the paper [GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data](https://arxiv.org/abs/1705.04932). All the experiments are initially done in our proprietary deep learning framework. For convenience, we reproduce the results using TensorFlow. <div align="center"> <img align="center" src="images/cross.jpg" width="450" alt="cross"> </div> <br/> 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 a 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 <div align="center"> <img align="center" src="images/182929_resize.jpg" alt="input"> <img align="center" src="images/022344_resize.jpg" alt="target"> <img align="center" src="images/swap_out1.jpg" alt="out1"> <img align="center" src="images/swap_out2.jpg" alt="out2"> </div> <div align="center"> Swap Attribute </div> <br/> #### 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 <div align="center"> <img align="center" src="images/interpolation.jpg" alt="interpolation"> <img align="center" src="images/035460_resize.jpg" alt="target"> </div> <div align="center"> Linear Interpolation </div> <br/> #### 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 <div align="center"> <img align="center" src="images/four_matrix.jpg" alt="matrix"> </div> <div align="center"> Matrix Interpolation </div> <br/>