You can skip this data preparation procedure if directly using the tf-record data files.
cd datasets
./run_convert_market.sh to download and convert the original images, poses, attributes, segmentations
./run_convert_DF.sh to download and convert the original images, poses
Note: we also provide the convert code for Market-1501 Attribute and Market-1501 Segmentation results from PSPNet. These extra info, are provided for further research. In our experiments, pose mask are obtained from pose key-points (see _getPoseMask function in convert .py files).
Training steps
Download the tf-record training data.
Modify the model_dir in the run_market_train.sh/run_DF_train.sh scripts.
run run_market_train.sh/run_DF_train.sh
Note: we use a triplet instead of pair real/fake for adversarial training to keep training more stable.
Testing steps
Download the pretrained models and tf-record testing data.
Modify the model_dir in the run_market_test.sh/run_DF_test.sh scripts.
@inproceedings{ma2017pose,
title={Pose guided person image generation},
author={Ma, Liqian and Jia, Xu and Sun, Qianru and Schiele, Bernt and Tuytelaars, Tinne and Van Gool, Luc},
booktitle={Advances in Neural Information Processing Systems},
pages={405--415},
year={2017}
}