[ACM MM 2019 Oral] Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation

Contents

• Cycle-In-Cycle GANs
• Installation
• Dataset Preparation
• Generating Images Using Pretrained Model
• Train and Test New Models
• Acknowledgments
• Related Projects
• Citation
• Contributions

Cycle-In-Cycle GANs

| Conference Paper | Extended Paper | Project |
Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation
Hao Tang¹, Dan Xu², Gaowen Liu³, Wei Wang⁴, Nicu Sebe¹ and Yan Yan^3
1University of Trento, ²University of Oxford, ³Texas State University, ⁴EPFL
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, ECCV 2020 paper XingGAN for Person Image Generation, and ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.

C2GAN Framework

License

Copyright (C) 2019 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/C2GAN
cd C2GAN/

This code requires PyTorch 0.4.1+ and python 3.6.9+. Please install dependencies by

pip install -r requirements.txt (for pip users)

or

./scripts/conda_deps.sh (for Conda users)

To reproduce the results reported in the paper, you would need an NVIDIA TITAN Xp GPUs.

Dataset Preparation

For your convenience we provide download scripts:

bash ./datasets/download_c2gan_dataset.sh RaFD_image_landmark

• RaFD_image_landmark: 3.0 GB

or you can use ./scripts/convert_pts_to_figure.m to convert the generated pts files to figures.

Prepare the datasets like in this folder after the download has finished. Please cite their paper if you use the data.

Generating Images Using Pretrained Model

• You need download a pretrained model (e.g., Radboud) with the following script:

bash ./scripts/download_c2gan_model.sh Radboud

• The pretrained model is saved at ./checkpoints/{name}_pretrained/latest_net_G.pth.
• Then generate the result using

python test.py --dataroot ./datasets/Radboud --name Radboud_pretrained --model c2gan --which_model_netG unet_256 --which_direction AtoB --dataset_mode aligned --norm batch --gpu_ids 0 --batch 16;

The results will be saved at ./results/. Use --results_dir {directory_path_to_save_result} to specify the results directory.

• For your own experiments, you might want to specify --netG, --norm, --no_dropout to match the generator architecture of the trained model.

Train and Test New Models

• Download a dataset using the previous script (e.g., Radboud).
• To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097.
• Train a model:

sh ./train_c2gan.sh

• To see more intermediate results, check out ./checkpoints/Radboud_c2gan/web/index.html.
• Test the model:

sh ./test_c2gan.sh

• The test results will be saved to a html file here: ./results/Radboud_c2gan/latest_test/index.html.

Acknowledgments

This source code is inspired by Pix2pix, and GestureGAN.

Related Projects

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

Citation

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

C2GAN

@article{tang2021total,
  title={Total Generate: Cycle in Cycle Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural Scenes},
  author={Tang, Hao and Sebe, Nicu},
  journal={IEEE Transactions on Multimedia (TMM)},
  year={2021}
}

@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}
}

If you use the original BiGraphGAN, XingGAN, GestureGAN, 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}
}

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}
}

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}
}

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]).

Collaborations

I'm always interested in meeting new people and hearing about potential collaborations. If you'd like to work together or get in contact with me, please email [email protected]. Some of our projects are listed here.

If you can do what you do best and be happy, you're further along in life than most people.