PISE

The code is based on the CVPR paper PISE: Person Image Synthesis and Editing with Decoupled GAN, Project Page, supp.

Requirement

conda create -n pise python=3.6
conda install pytorch=1.2 cudatoolkit=10.0 torchvision
pip install scikit-image pillow pandas tqdm dominate natsort 

Data

Data preparation for images and keypoints can follow Pose Transfer and GFLA.

1. Download deep fashion dataset. You will need to ask a password from dataset maintainers. Unzip 'Img/img.zip' and put the folder named 'img' in the './fashion_data' directory.

2. Download train/test key points annotations and the dataset list from Google Drive, including fashion-pairs-train.csv, fashion-pairs-test.csv, fashion-annotation-train.csv, fashion-annotation-train.csv, train.lst, test.lst. Put these files under the ./fashion_data directory.

3. Run the following code to split the train/test dataset.

   python data/generate_fashion_datasets.py
   

4. Download parsing data, and put these files under the ./fashion_data directory. Parsing data for testing can be found from baidu (fectch code: abcd) or Google drive. Parsing data for training can be found from baidu (fectch code: abcd) or Google drive. You can get the data follow with PGN, and re-organize the labels as you need.

Train

bash ./scripts/train.sh

Note that if you want to train a pose transfer model as well as texture transfer and region editing, just comments the line 177 and 178, and uncomments line 162-176.

For training using multi-gpus, you can refer to issue in GFLA

Test

You can directly download our test results from baidu (fetch code: abcd) or Google drive.
Pre-trained checkpoint of human pose transfer reported in our paper can be found from baidu (fetch code: abcd) or Google drive and put it in the folder (-->results-->fashion).

Pre-Trained checkpoint of texture transfe, region editing, style interpolation used in our paper can be found from baidu(fetch code: abcd) or Google drive. Note that the model need to be changed.

Test by yourself

bash ./scripts/test.sh

Acknowledgments

Our code is based on GFLA.