# Awesome Virtual Tryon
## Visual Comparison
We provide visualization results of various state-of-the-art methods to facilitate your experimental comparisons.
[Visualization results](https://drive.google.com/file/d/1loiMvddHoRi7-eBz4qy45f3CgfFiGCyT/view?usp=sharing) of CP-VTON, CP-VTON+, ClothFlow, ACGPN, PF-AFN, DCTON, RT-VTON and our DOC-VTON.
<p float="center">
<img src="Visual_comparison.png" width="800px"/>
------
| Model | Published | Code | FID |
| ----------------- | -------------------------------------------- | :----------------------------------------------------------- | ------------------------------------------------------------ |
| CP-VTON | [ECCV2018](https://arxiv.org/pdf/1807.07688.pdf) | [Code](https://github.com/sergeywong/cp-vton) | 24.43 |
| CP-VTON+ | [CVPRW2020](https://minar09.github.io/cpvtonplus/cvprw20_cpvtonplus.pdf) | [Code](https://github.com/minar09/cp-vton-plus) | 21.08 |
| ClothFlow | [ICCV2019](https://openaccess.thecvf.com/content_ICCV_2019/papers/Han_ClothFlow_A_Flow-Based_Model_for_Clothed_Person_Generation_ICCV_2019_paper.pdf) | - | 14.43 |
| ACGPN | [CVPR2020](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_Towards_Photo-Realistic_Virtual_Try-On_by_Adaptively_Generating-Preserving_Image_Content_CVPR_2020_paper.pdf) | [Code](https://github.com/switchablenorms/DeepFashion_Try_On) | 15.67 |
| DCTON | [CVPR2021](https://openaccess.thecvf.com/content/CVPR2021/papers/Ge_Disentangled_Cycle_Consistency_for_Highly-Realistic_Virtual_Try-On_CVPR_2021_paper.pdf) | [Code](https://github.com/ChongjianGE/DCTON) | 14.82 |
| PF-AFN | [CVPR2021](https://openaccess.thecvf.com/content/CVPR2021/papers/Ge_Parser-Free_Virtual_Try-On_via_Distilling_Appearance_Flows_CVPR_2021_paper.pdf) | [Code](https://github.com/geyuying/PF-AFN) | 10.09 |
| RT-VTON | [CVPR2022](https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Full-Range_Virtual_Try-On_With_Recurrent_Tri-Level_Transform_CVPR_2022_paper.pdf) | - | 11.66 |
| DOC-VTON | [TMM2023](https://arxiv.org/pdf/2301.00965.pdf) | [Code](https://github.com/JyChen9811/DOC-VTON) | 9.54 |
------
## Auxiliary test data
We provide [densepose results](https://drive.google.com/file/d/1LiiuKvNLTtmQ3WKSxpLlP8NL10fO04UT/view?usp=sharing) of VITON test imgs.
We reprocess the densepose results and human parsing results of VITON-HD (Training and Testing dataset). You can download them through [Baiduyun](https://pan.baidu.com/s/1kEbYMfehiaiq4YXPX19DOg?pwd=deh8). PWD: deh8.
## Tips for Coding
We recommend using [PF-AFN](https://github.com/geyuying/PF-AFN) as codebase, which contains tensorboard, DDP training set, and nice code.
# DOC-VTON
## Official Codes for OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup (TMM 2023)
## Our Environment
anaconda3
pytorch 1.1.0
torchvision 0.3.0
cuda 9.0
cupy 6.0.0
opencv-python 4.5.1
python 3.6
## Installation
conda create -n tryon python=3.6
source activate tryon or conda activate tryon
conda install pytorch=1.1.0 torchvision=0.3.0 cudatoolkit=9.0 -c pytorch
conda install cupy or pip install cupy==6.0.0
pip install opencv-python
## Test Script
python test_w_enhance.py --name demo --resize_or_crop None --batchSize 1 --gpu_ids=1
## Checkpoints Downlowd Address
[Checkpoints for Test](https://drive.google.com/file/d/1yj8khxliGEcEfFLhT_NJ3zUYN1UlV1t1/view?usp=sharing)
## License
The use of this code is RESTRICTED to non-commercial research and educational purposes.
## Citation
Please cite if our work is useful for your research:
```
@article{2023occlumix,
title={OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup},
author={Yang, Zhijing and Chen, Junyang and Shi, Yukai and Li, Hao and Chen, Tianshui and Lin, Liang},
journal={arXiv preprint arXiv:2301.00965},
year={2023}
}
```
```
@article{2023occlumix,
author={Yang, Zhijing and Chen, Junyang and Shi, Yukai and Li, Hao and Chen, Tianshui and Lin, Liang},
journal={IEEE Transactions on Multimedia},
title={OccluMix: Towards De-Occlusion Virtual Try-On by Semantically-Guided Mixup},
year={2023},
volume={},
number={},
pages={1-12},
doi={10.1109/TMM.2023.3234399}}
```
文章OccluMix的源码
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2023-11-25
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OccluMix-main.zip （56个子文件） 
DOC-VTON-main 
test_pairs.txt 52KB data 
__init__.py 11B base_data_loader.py 195B base_dataset.py 4KB data_loader_test.py 234B custom_dataset_data_loader_test.py 876B aligned_dataset_test.py 4KB image_folder.py 2KB aligned_dataset_train.py 4KB __pycache__ custom_dataset_data_loader_test.cpython-36.pyc 1KB data_loader_test.cpython-37.pyc 409B base_dataset.cpython-36.pyc 4KB image_folder.cpython-36.pyc 3KB image_folder.cpython-37.pyc 3KB base_data_loader.cpython-37.pyc 675B aligned_dataset_test.cpython-37.pyc 2KB custom_dataset_data_loader_test.cpython-37.pyc 1KB data_loader_test.cpython-36.pyc 430B base_data_loader.cpython-36.pyc 696B __init__.cpython-37.pyc 134B aligned_dataset_test.cpython-36.pyc 3KB __init__.cpython-36.pyc 155B base_dataset.cpython-37.pyc 4KB options __init__.py 14B test_options.py 615B base_options.py 3KB __pycache__ base_options.cpython-36.pyc 3KB test_options.cpython-37.pyc 826B base_options.cpython-37.pyc 3KB __init__.cpython-37.pyc 137B test_options.cpython-36.pyc 847B __init__.cpython-36.pyc 158B test_w_enhance.py 10KB
Visual_comparison.png 2.94MB models unet_parts.py 2KB afwm.py 8KB networks.py 11KB correlation __pycache__ correlation.cpython-37.pyc 13KB correlation.cpython-36.pyc 13KB README.md 435B correlation.py 14KB __pycache__ unet_parts.cpython-36.pyc 3KB unet2.cpython-36.pyc 5KB networks.cpython-37.pyc 6KB afwm.cpython-36.pyc 7KB networks.cpython-36.pyc 7KB afwm.cpython-37.pyc 7KB unet2.py 4KB util __init__.py 11B util.py 6KB image_pool.py 1KB __pycache__ util.cpython-36.pyc 7KB __init__.cpython-37.pyc 134B __init__.cpython-36.pyc 155B util.cpython-37.pyc 4KB README.md 5KB资源评论
