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VTON-IT: Virtual Try-On using Image Translation
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This paper introduces VTON-IT, a novel Virtual Try-On application that uses semantic segmentation and a generative adversarial network to produce high-resolution, natural-looking images of clothes overlaid onto segmented body regions, addressing the challenges of body size, pose, and occlusions.
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+Below is the overview of proposed VTON-IT. First, the human body is detected and cropped. Then, the desired body region is segmented through U2-Net architecture and the segmented mask is fed to the image translation network to generate wrapped cloth. Finally, the wrapped cloth is overlayed over the input image.
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+## Result
+For evaluating the performance of VTON-IT through visual observation, we compared the final overlayed images with the output of CP-VTON+. Image below shows that the proposed virtual try-on application produces more realistic and convincing results in terms of texture transfer quality and pose preservation.
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![](final_overlay.jpg)
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## Requirements
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- opencv
## Training Pix2pix:
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```
- python3 train.py --label_nc 0 --no_instance --name vd2.0_2 --dataroot ./datasets/vd2.0_2 --continue_train --gpu_ids 0,1 --batchSize 2
+ python3 train.py --label_nc 0 --no_instance --name vd2.0_2 --dataroot ./datasets/vd2.0_2 --continue_train --gpu_ids 0,1 --batchSize 2
```
## Train Segmentation model
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```
u2net_train.py
```
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## Inference
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```
Inference.py
```
## Reference
-If you find this repo helpful, please consider citing:
+If you find our paper and code useful in your research, please consider giving a star ⭐ and citation 📝 :)
```
@misc{adhikari2023vtonit,
- title={VTON-IT: Virtual Try-On using Image Translation},
+ title={VTON-IT: Virtual Try-On using Image Translation},
author={Santosh Adhikari and Bishnu Bhusal and Prashant Ghimire and Anil Shrestha},
year={2023},
eprint={2310.04558},
@@ -62,10 +72,5 @@ If you find this repo helpful, please consider citing:
```
## Acknowledgements
- The authors would like to thank IKebana Solutions LLC for providing them with constant support for this research project.
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+The authors would like to thank IKebana Solutions LLC for providing them with constant support for this research project.
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