CoCosNet

Pytorch Implementation of the paper "Cross-domain Correspondence Learning for Exemplar-based Image Translation" (CVPR 2020 oral).

Update:

20200525: Training code for deepfashion complete. Due to the memory limitations, I employed the following conversions:

• Disable the non-local layer, as the memory cost is infeasible on common hardware. If the original paper is telling the truth that the non-lacal layer works on (128-128-256) tensors, then each attention matrix would contain 128^4 elements (which takes 1GB).
• Shrink the correspondence map size from 64 to 32, leading to 4x memory save on dense correspondence matrices.
• Shrink the base number of filters from 64 to 16.

The truncated model barely fits in a 12GB GTX Titan X card, but the performance would not be the same.

Environment

• Ubuntu/CentOS
• Pytorch 1.0+
• opencv-python
• tqdm

TODO list

• Prepare dataset
• Implement the network
• Implement the loss functions
• Implement the trainer
• Training on DeepFashion
• Adjust network architecture to satisfy a single 16 GB GPU.
• Training for other tasks

Dataset Preparation

DeepFashion

Just follow the routine in the PATN repo

Pretrained Model

The pretrained model for human pose transfer task: TO BE RELEASED

Training

run python train.py.

Citations

If you find this repo useful for your research, don't forget to cite the original paper:

@article{Zhang2020CrossdomainCL,
  title={Cross-domain Correspondence Learning for Exemplar-based Image Translation},
  author={Pan Zhang and Bo Zhang and Dong Chen and Lu Yuan and Fang Wen},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.05571}
}

Acknowledgement

TODO.