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PyTorch Implementation for Unsupervised Person Image Generation with Semantic Parsing Transformation
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README.md

person_generation_spt

Example

Unsupervised Person Image Generation with Semantic Parsing Transformation
(CVPR 2019, oral).

Sijie Song, Wei Zhang, Jiaying Liu, Tao Mei

Project page: http://39.96.165.147/Projects/SijieSong_cvpr19/CVPR19_ssj.html

Check our our paper and supplementary here.

Prerequisites

  • Python 2 (Python 3 should also work, but needs some modification)
  • Pytorch >= 0.4.0
  • opencv-python
  • Numpy
  • Scipy
  • Pandas
  • Skimage

Getting started

A demo model is given for appearance generation. We provide some samples in "./imgs", the parsing maps are in "./parsing".

  • Clone this repo:
git clone https://github.com/SijieSong/person_generation_spt.git

cd person_generation_spt
  • Download pre-trained models from Google Drive, put ./demo_model under ./checkpoints

  • Quick testing (modify the gpu_id in ./scripts/test_demo.sh if needed)

bash ./scripts/test_demo.sh
  • Check the results in ./results/demo_test (source image | target pose (groud truth) | output)

    Example

Citation

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

@inproceedings{song2019unsupervised,
  title={Unsupervised Person Image Generation with Semantic Parsing Transformation},
  author={Song, Sijie and Zhang, Wei and Liu, Jiaying and Mei, Tao},
  booktitle = {Proc.~IEEE Conference on Computer Vision and Pattern Recognition},
  year={2019}
}

Related projects

Contact

Sijie Song ssj940920 AT pku.edu.cn

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