Pytorch implementation of CVPR2021 paper "MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation"

Overview

MUST-GAN

Code | paper

The Pytorch implementation of our CVPR2021 paper "MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation".

Tianxiang Ma, Bo Peng, Wei Wang, Jing Dong,

CRIPAC,NLPR,CASIA & University of Chinese Academy of Sciences.


Test results of our model under self-supervised training:

Pose transfer

Clothes style transfer

Requirement

  • python3
  • pytorch 1.1.0
  • numpy
  • scipy
  • scikit-image
  • pillow
  • pandas
  • tqdm
  • dominate
  • visdom

Getting Started

Installation

  • Clone this repo:
git clone https://github.com/TianxiangMa/MUST-GAN.git
cd MUST-GAN

Data Preperation

We train and test our model on Deepfashion dataset. Especially, we utilize High-Res Images in the In-shop Clothes Retrieval Benchmark.

Download this dataset and unzip (You will need to ask for password.) it, then put the folder img_highres under the ./datasets directory. Download train/test split list, which are used by a lot of methods, and put them under ./datasets directory.

  • Run the following code to split train/test dataset.
python tool/generate_fashion_datasets.py

Download source-target paired images list, as same as the list used by many previous work. Becouse our method can self-supervised training, we do not need the fashion-resize-pairs-train.csv, you can download train_images_lst.csv for training.

Download train/test keypoints annotation files and semantic segmentation files.

Put all the above files into the ./datastes folder.

  • Run the following code to generate pose map and pose connection map.
python tool/generate_pose_map.py
python tool/generate_pose_connection_map.py

Download vgg pretrained model for training, and put it into ./datasets folder.

Test

Download our pretrained model, and put it into ./check_points/MUST-GAN/ folder.

  • Run the following code, and set the parameters as your need.
bash scripts/test.sh

Train

  • Run the following code, and set the parameters as your need.
bash scripts/train.sh

Citation

If you use this code for your research, please cite our paper:

@InProceedings{Ma_2021_CVPR,
    author    = {Ma, Tianxiang and Peng, Bo and Wang, Wei and Dong, Jing},
    title     = {MUST-GAN: Multi-Level Statistics Transfer for Self-Driven Person Image Generation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {13622-13631}
}

Acknowledgments

Our code is based on PATN and ADGAN, thanks for their great work.

Owner
TianxiangMa
Ph.D. Candidate. Current research interests mainly lie in the fields of deep learning, especially applying generative adversarial models to computer vision.
TianxiangMa
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