CP-VTON+ (CVPRW 2020)

Official implementation for "CP-VTON+: Clothing Shape and Texture Preserving Image-Based Virtual Try-On" from CVPRW 2020.
Project page: https://minar09.github.io/cpvtonplus/.
Saved/Pre-trained models: Checkpoints

Usage

1. Install the requirements
2. Prepare the dataset
3. Train GMM network
4. Get warped clothes for training set with trained GMM network, and copy inside data/viton_resize/train directory
5. Train TOM network
6. Test/evaluate with test set

Installation

This implementation is built and tested in PyTorch 0.4.1.
Run pip install -r requirements.txt

Data preparation

1. Run python data_download.py
2. Run python dataset_neck_skin_correction.py
3. Run python body_binary_masking.py

Training

Run python train.py with your specific usage options for GMM and TOM stage.
For example, GMM: python train.py --name GMM --stage GMM --workers 4 --save_count 5000 --shuffle
and for TOM stage, python train.py --name TOM --stage TOM --workers 4 --save_count 5000 --shuffle

Testing

Run 'python test.py' with your specific usage options.
For example, GMM: python test.py --name GMM --stage GMM --workers 4 --datamode test --data_list test_pairs.txt --checkpoint checkpoints/GMM/gmm_final.pth
and for TOM stage: python test.py --name TOM --stage TOM --workers 4 --datamode test --data_list test_pairs.txt --checkpoint checkpoints/TOM/tom_final.pth

Inference/Demo

Download the pre-trained models from here: Checkpoints. Then run the same step as Testing to test our model.

Citation

Please cite our paper in your publications if it helps your research:

@InProceedings{Minar_CPP_2020_CVPR_Workshops,
	title={CP-VTON+: Clothing Shape and Texture Preserving Image-Based Virtual Try-On},
	author={Minar, Matiur Rahman and Thai Thanh Tuan and Ahn, Heejune and Rosin, Paul and Lai, Yu-Kun},
	booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
	month = {June},
	year = {2020}
}

Acknowledgements

This implementation is largely based on the PyTorch implementation of CP-VTON. We are extremely grateful for their public implementation.