mrtechnoo / cp-vton-plus

Official implementation for "CP-VTON+: Clothing Shape and Texture Preserving Image-Based Virtual Try-On", CVPRW 2020

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

This pipeline is a combination of consecutive training and testing of GMM + TOM. GMM generates the warped clothes according to the target human. Then, TOM blends the warped clothes outputs from GMM into the target human properties, to generate the final try-on output.

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 warped clothes & masks inside data/train directory
5. Train TOM network
6. Test/evaluate with test set, test GMM first, then copy warped clothes & masks inside data/test directory, and test TOM

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, it will download the full dataset into data/ folder, both train and test.
2. Run python dataset_neck_skin_correction.py, twice for both 'train' and 'test' data for training and testing. Please set the correct paths in the script before running. It will add a new segmentation label for neck/skin areas, and save the new segmentation in "image-parse-new" folder. You need to run twice for both train and test data. We also uploaded the corrected segmentation here: image-parse-new
3. Run python body_binary_masking.py, twice for both 'train' and 'test' data for training and testing. Please set the correct paths in the script before running. It will create the body binary masks for the inputs of the networks. You need to run twice for both train and test data.

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
Then run test.py for GMM network with the training dataset, which will generate the warped clothes and masks in "warp-cloth" and "warp-mask" folders inside the "result/GMM/train/" directory. Copy the "warp-cloth" and "warp-mask" folders into your data directory, for example inside "data/train" folder.
Run 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
Then run test.py for GMM network with the testing dataset, which will generate the warped clothes and masks in "warp-cloth" and "warp-mask" folders inside the "result/GMM/test/" directory. Copy the "warp-cloth" and "warp-mask" folders into your data directory, for example inside "data/test" folder.
Run 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.