文件列表:

data_loader.py (4164, 2018-09-07)
data_loader_evaluate.py (3823, 2018-09-07)
evaluate.py (3612, 2018-09-07)
figures (0, 2018-09-07)
figures\intro.png (825963, 2018-09-07)
list.txt (7272289, 2018-09-07)
list_test.txt (75, 2018-09-07)
model.py (5932, 2018-09-07)
test_data (0, 2018-09-07)
test_data\080004_128.jpg (3696, 2018-09-07)
test_data\116669_128.jpg (3972, 2018-09-07)
test_data\139099_128.jpg (3552, 2018-09-07)
train.py (11216, 2018-09-07)

# CR-GAN: Learning Complete Representations for Multi-view Generation Training code for the paper **[CR-GAN: Learning Complete Representations for Multi-view Generation](https://arxiv.org/abs/1806.11191.pdf)**, IJCAI 2018 Project page can be found here: https://sites.google.com/site/xipengcshomepage/research/ijcai18 ## Overview Prior works use "Encoder-Generator-Discriminator" framework to generate multi-view images for a single view input. Where training data is first mapped to a subspace via encoder, then the generator learns multi-view generation within this subspace. It lacks the ability to deal with new data, as an "unseen" data may be mapped out of the subspace, and the generator behavior for this case is undefined. We propose a two-pathway framework to address this problem. Generation path is introduced to let generator generates in whole space. Reconstruction path is used to reconstruct all training data.

### Prerequisites This package has the following requirements: * `Python 2.7` * `Pytorch 0.3.1` ## Dataset Multi-PIE dataset webpage: http://www.cs.cmu.edu/afs/cs/project/PIE/MultiPie/Multi-Pie/Home.html Our cropped version can be downloaded at: https://drive.google.com/open?id=1QxNCh6vfNSZkod1Rg_zHLI1FM8WyXix4 300w-LP dataset webpage: http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm Our cropped version can be downloaded at: https://drive.google.com/open?id=1DD6AO9Y5rAgiiW7IJY2kBxI_bCcfhYo4 For 300w-LP dataset, data_loader.py need additional text files to get the view label, text files can be downloaded here: https://drive.google.com/open?id=1TIfcpn4N3rgGlzWl0lXNZKhy7XWVWOoA Please cite the papers if using those dataset: (Multi-PIE) Ralph Gross, Iain Matthews, JeffreyCohn, Takeo Kanade, and Simon Baker. Multi-pie.ImageVision Computer, 28(5):807“813, 2010. (300w-LP) X. Zhu, Z. Lei, X. Liu, H. Shi, and S.Z. Li.Face Alignment Across Large Poses: A 3D Solution. In CVPR, 2016. ## Training python train.py ## Pre-trained model Pre-trained model can be downloaded here: https://drive.google.com/open?id=1J3VffWKe8akdiNM2hy7NI3lY4xM_xL-c Run test images: python evaluate.py ## Results Face rotation:  Face attribute manipulation:  ## Citation If you find this code useful in your research, please consider citing: ``` @article{tian2018cr, title={Cr-gan: Learning complete representations for multi-view generation}, author={Tian, Yu and Peng, Xi and Zhao, Long and Zhang, Shaoting and Metaxas, Dimitris N}, journal={arXiv preprint arXiv:1806.11191}, year={2018} } ```

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