Reference: Tensorflow implementation of NIPS 2017 paper Pose Guided Person Image Generation

• python 2.7
• tensorflow-gpu (1.4.1)
• numpy (1.14.0)
• Pillow (5.0.0)
• scikit-image (0.13.0)
• scipy (1.0.1)
• matplotlib (2.0.0)

• None as now

You can skip this data preparation procedure if directly using the tf-record data files.

1. cd datasets
2. ./run_convert_market.sh to download and convert the original images, poses, attributes, segmentations
3. ./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 ontained from pose key-points (see _getPoseMask function in convert .py files).

1. Download the tf-record training data.
2. Modify the model_dir in the run_market_train.sh/run_DF_train.sh scripts.
3. 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.

1. Download the pretrained models and tf-record testing data.
2. Modify the model_dir in the run_market_test.sh/run_DF_test.sh scripts.
3. run run_market_test.sh/run_DF_test.sh

@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}
}