Tensorflow implementation of CVPR 2018 paper "Disentangled Person Image Generation"
Tensorflow implementation of CVPR 2018 paper Disentangled Person Image Generation
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./run_convert_RCV.sh
to convert the original images and pose coordinates, i.e. (row, column, visibility)
(e.g. from OpenPose or MaskRCNN), which can be useful for other datasets.
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).log_dir
and log_dir_pretrain
in the run_market_train.sh/run_DF_train.sh scripts.Note: we use a triplet instead of pair real/fake for adversarial training to keep training more stable.
log_dir
and log_dir_pretrain
in the run_market_test.sh/run_DF_test.sh scripts.@inproceedings{ma2018disentangled,
title={Disentangled Person Image Generation},
author={Ma, Liqian and Sun, Qianru and Georgoulis, Stamatios and Van Gool, Luc and Schiele, Bernt and Fritz, Mario},
booktitle={{IEEE} Conference on Computer Vision and Pattern Recognition},
year={2018}
}