Training code for the paper CR-GAN: Learning Complete Representations for Multi-view Generation, IJCAI 2018
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.
This package has the following requirements:
Python 2.7Pytorch 0.3.1
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
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.
python train.py
Pre-trained model can be downloaded here:
https://drive.google.com/open?id=1J3VffWKe8akdiNM2hy7NI3lY4xM_xL-c
Run test images:
python evaluate.py
Face rotation:
Face attribute manipulation:
