The code for our CVPR paper PISE: Person Image Synthesis and Editing with Decoupled GAN, Project Page, supp.

• Added features:

  • Training on iPER dataset (https://svip-lab.github.io/dataset/iPER_dataset.html)

  • Online training pairs sampling

    • Random sampling trianing pairs instead of loading fixed pairs from predetermined training csv files. For any sampled training pair (A, B), the appearances are the same (actor & cloth). However in iPER, each actor with same cloth has two different videos: random pose & fixed pose with varying azimuthal angles. Therefore, a training pair can be sampled from two different videos.

  • Testing on iPER dataset

  • Testing with different csv image pair files, saving results automatically with corresponding names

  • demo.py: A separate demo pipline.

    • Given any input video, bone poses and an source image, output an pose transfered video

    • Modified dataset setup process so that it doesn't require segmentations from demo video images

    • Generating testing image pairs on the fly to eliminate the need for preconfigured csv pair files.

  • Offline reorganizing datasets (deepfashion, iPER) for faster data loading

  • Saving visualization triple (ref, output, gt)

  • Support training with png images

• Resolved issues from original code:

  • incorrect loading pretrained vgg19 from author's local machine

  • deprecated usage of .cuda(, async=True)

  • incorrect --store_input default value for visualization

  • removed default batch_size=1 during testing for faster inference

  • deprecated fill argument in F.affine()

• Code cleaning

  • Cleaned over 150+ lines of redundent or unnecessary code including some core modules such as self-attention.

  • Variables renaming for better readability

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 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.

python train.py --name=fashion --model=painet --gpu_ids=0

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

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

python test.py --name=fashion --model=painet --gpu_ids=0 

If you use this code, please cite our paper.

@InProceedings{Zhang_2021_CVPR,
    author    = {Zhang, Jinsong and Li, Kun and Lai, Yu-Kun and Yang, Jingyu},
    title     = {{PISE}: Person Image Synthesis and Editing With Decoupled GAN},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {7982-7990}
}

Our code is based on GFLA.