Download the pretrained model and then generate images.

bash ./demo.sh

[Paper]

Yixiao Ge*, Zhuowan Li*, Haiyu Zhao, Guojun Yin, Shuai Yi, Xiaogang Wang, and Hongsheng Li
Neural Information Processing Systems (NIPS), 2018 (* equal contribution)

Pytorch implementation for our NIPS 2018 work. With the proposed siamese structure, we are able to learn identity-related and pose-unrelated representations.

• Python 3
• Pytorch (We run the code under version 0.3.1, maybe lower versions also work.)

• Install dependencies (e.g., visdom and dominate). You can install all the dependencies by:

pip install scipy, pillow, torchvision, sklearn, h5py, dominate, visdom

• Clone this repo:

git clone https://github.com/yxgeee/FD-GAN
cd FD-GAN/

We conduct experiments on Market1501, DukeMTMC, CUHK03 datasets. We need pose landmarks for each dataset during training, so we generate the pose files by Realtime Multi-Person Pose Estimation. And the raw datasets have been preprocessed by the code in open-reid. Download the prepared datasets following below steps:

• Create directories for datasets:

mkdir datasets
cd datasets/

• Download these datasets through the links above, and unzip them in the same root path.

As mentioned in the original paper, there are three stages for training our proposed framework.

bash ./demo.sh

• Market1501_stageIII_model
• DukeMTMC_stageIII_model
• CUHK03_stageIII_model

And test best_net_E.pth by the same way as mentioned in Stage I.

Please cite our paper if you find the code useful for your research.

@inproceedings{ge2018fdgan,
  title={FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification},
  author={Ge, Yixiao and Li, Zhuowan and Zhao, Haiyu and Yin, Guojun and Wang, Xiaogang and Li, Hongsheng},
  booktitle={Advances in Neural Information Processing Systems},
  year={2018}
}

Our code is inspired by pytorch-CycleGAN-and-pix2pix and open-reid.