Ha0Tang / Xinggan

Contents

• XingGAN or CrossingGAN
• Installation
• Dataset Preparation
• Generating Images Using Pretrained Model
• Train and Test New Models
• Evaluation
• Acknowledgments
• Related Projects
• Citation
• Contributions

XingGAN or CrossingGAN

| Project | Paper |
XingGAN for Person Image Generation
Hao Tang¹², Song Bai², Li Zhang², Philip H.S. Torr², Nicu Sebe¹³.
¹University of Trento, Italy, ²University of Oxford, UK, ³Huawei Research Ireland, Ireland.
In ECCV 2020.
The repository offers the official implementation of our paper in PyTorch.

In the meantime, check out our related BMVC 2020 oral paper Bipartite Graph Reasoning GANs for Person Image Generation.

Framework

Comparison Results

License

Copyright (C) 2020 University of Trento, Italy.

All rights reserved. Licensed under the CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International)

The code is released for academic research use only. For commercial use, please contact [email protected].

Installation

Clone this repo.

git clone https://github.com/Ha0Tang/XingGAN
cd XingGAN/

This code requires PyTorch 1.0.0 and python 3.6.9+. Please install the following dependencies:

• pytorch 1.0.0
• torchvision
• numpy
• scipy
• scikit-image
• pillow
• pandas
• tqdm
• dominate

To reproduce the results reported in the paper, you need to run experiments on NVIDIA DGX1 with 4 32GB V100 GPUs for DeepFashion, and 1 32GB V100 GPU for Market-1501.

Dataset Preparation

Please follow SelectionGAN to directly download both Market-1501 and DeepFashion datasets.

This repository uses the same dataset format as SelectionGAN and BiGraphGAN. so you can use the same data for all these methods.

Generating Images Using Pretrained Model

Market-1501

sh scripts/download_xinggan_model.sh market

Then,

1. Change several parameters in test_market.sh.
2. Run sh test_market.sh for testing.

DeepFashion

sh scripts/download_xinggan_model.sh deepfashion

Then,

1. Change several parameters in test_deepfashion.sh.
2. Run sh test_deepfashion.sh for testing.

Train and Test New Models

Market-1501

1. Change several parameters in train_market.sh.
2. Run sh train_market.sh for training.
3. Change several parameters in test_market.sh.
4. Run sh test_market.sh for testing.

DeepFashion

1. Change several parameters in train_deepfashion.sh.
2. Run sh train_deepfashion.sh for training.
3. Change several parameters in test_deepfashion.sh.
4. Run sh test_deepfashion.sh for testing.

Evaluation

We adopt SSIM, mask-SSIM, IS, mask-IS, and PCKh for evaluation of Market-1501. SSIM, IS, PCKh for DeepFashion.

1. SSIM, mask-SSIM, IS, mask-IS: install python3.5, tensorflow 1.4.1, and scikit-image==0.14.2. Then run, python tool/getMetrics_market.py or python tool/getMetrics_fashion.py.

2. PCKh: install python2, and pip install tensorflow==1.4.0, then set export KERAS_BACKEND=tensorflow. After that, run python tool/crop_market.py or python tool/crop_fashion.py. Next, download pose estimator and put it under the root folder, and run python compute_coordinates.py. Lastly, run python tool/calPCKH_market.py or python tool/calPCKH_fashion.py.

Please refer to Pose-Transfer for more details.

Acknowledgments

This source code is inspired by both Pose-Transfer and SelectionGAN.

Related Projects

BiGraphGAN | GestureGAN | C2GAN | SelectionGAN | Guided-I2I-Translation-Papers

Citation

If you use this code for your research, please cite our paper.

XingGAN

@inproceedings{tang2020xinggan,
  title={XingGAN for Person Image Generation},
  author={Tang, Hao and Bai, Song and Zhang, Li and Torr, Philip HS and Sebe, Nicu},
  booktitle={ECCV},
  year={2020}
}

If you use the original BiGraphGAN, GestureGAN, C2GAN, and SelectionGAN model, please cite the following papers:

BiGraphGAN

@inproceedings{tang2020bipartite,
  title={Bipartite Graph Reasoning GANs for Person Image Generation},
  author={Tang, Hao and Bai, Song and Torr, Philip HS and Sebe, Nicu},
  booktitle={BMVC},
  year={2020}
}

GestureGAN

@article{tang2019unified,
  title={Unified Generative Adversarial Networks for Controllable Image-to-Image Translation},
  author={Tang, Hao and Liu, Hong and Sebe, Nicu},
  journal={IEEE Transactions on Image Processing (TIP)},
  year={2020}
}

@inproceedings{tang2018gesturegan,
  title={GestureGAN for Hand Gesture-to-Gesture Translation in the Wild},
  author={Tang, Hao and Wang, Wei and Xu, Dan and Yan, Yan and Sebe, Nicu},
  booktitle={ACM MM},
  year={2018}
}

C2GAN

@inproceedings{tang2019cycleincycle,
  title={Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation},
  author={Tang, Hao and Xu, Dan and Liu, Gaowen and Wang, Wei and Sebe, Nicu and Yan, Yan},
  booktitle={ACM MM},
  year={2019}
}

SelectionGAN

@inproceedings{tang2019multi,
  title={Multi-channel attention selection gan with cascaded semantic guidance for cross-view image translation},
  author={Tang, Hao and Xu, Dan and Sebe, Nicu and Wang, Yanzhi and Corso, Jason J and Yan, Yan},
  booktitle={CVPR},
  year={2019}
}

@article{tang2020multi,
  title={Multi-channel attention selection gans for guided image-to-image translation},
  author={Tang, Hao and Xu, Dan and Yan, Yan and Corso, Jason J and Torr, Philip HS and Sebe, Nicu},
  journal={arXiv preprint arXiv:2002.01048},
  year={2020}
}

Contributions

If you have any questions/comments/bug reports, feel free to open a github issue or pull a request or e-mail to the author Hao Tang ([email protected]).