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Bi-level feature alignment for versatile image translation and manipulation [ECCV 2022]

Bi-level feature alignment for versatile image translation and manipulation

Preparation

Clone the Synchronized-BatchNorm-PyTorch repository.

cd models/networks/
git clone https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
cp -rf Synchronized-BatchNorm-PyTorch/sync_batchnorm .
cd ../../

VGG model for computing loss. Download from here, move it to models/.

Datasets

For the datasets for translation, please refer to CoCosNet.

For the datasets for image editing, you can download it from Google Drive.

Translation Results

Some prediction results of our model are provided in Google Drive.

Training

Then run the command

bash train_ade.sh

Citation

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

@article{zhan2021rabit,
  title={Bi-level feature alignment for versatile image translation and manipulation},
  author={Zhan, Fangneng and Yu, Yingchen and Wu, Rongliang and Cui, Kaiwen and Xiao, Aoran and Lu, Shijian and Shao, Ling},
  journal={arXiv preprint arXiv:2107.03021},
  year={2021}
}

Acknowledgments

This code borrows heavily from CoCosNet. We also thank SPADE, Synchronized Normalization.