person_generation_spt
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 out 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 or Baidu Yun, 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 (ground truth) | output)
-
Testing a new image:
You can test a new image with pre-defined parsing files (see the example in ./parsing). The id for each attribute label is defined as below: 0-background, 1-face, 2-hair, 3-upperclothes, 4-pants, 5-skirt, 6-leftArm, 7-rightArm, 8-leftLeg, 9-rightLeg.
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