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An image-based virtual try-on system with deep learning.
| Date | Stars |
|---|---|
| 2026-07-31 | 419 |
| 2026-08-01 | 419 |
| 2026-08-06 | 419 |
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# Deep Virtual Try-on with Clothes Transform Source code for paper "Deep Virtual Try-on with Clothes Transform" <img height="300" src="https://github.com/b01902041/Deep-Virtual-Try-on-with-Clothes-Transform/blob/master/readme_img/introduction.png"> ## Overall Architecture <img height="500" src="https://github.com/b01902041/Deep-Virtual-Try-on-with-Clothes-Transform/blob/master/readme_img/All.png"> ## Dependencies Install dependencies using pip. ```shell pip install -r requirements.txt ``` ## Step1: CAGAN <img height="200" src="https://github.com/b01902041/Deep-Virtual-Try-on-with-Clothes-Transform/blob/master/readme_img/CAGAN.png"> ### code and data ### * Training: `CAGAN.py` ``` python CAGAN.py ``` * Testing: `Testing_with_fixed_data.py` ``` python Testing_with_fixed_data.py ``` * Data: `MVC_image_pairs_resize_new.zip` ### parameters in code ### #### Training: CAGAN.py * Data should be put in `"./MVC_image_pairs_resize_new/1/*.jpg"` (for person images) `"./MVC_image_pairs_resize_new/5/*.jpg"` (for clothes images) > 470: data = "data folder name" > > 471: train_A = "person images folder name" > > 473: filenames_1 = "person images folder name" > > 474: filenames_5 = "clothes images folder name" > > 617, 618: set "save model path" #### Testing: Testing_with_fixed_data.py * Data should be put in `"./MVC_image_pairs_resize_new/1/*.jpg"` (for person images) `"./MVC_image_pairs_resize_new/5_test/*.jpg"` (for clothes images) >215: set "model path" > >220: data = "data folder name" > >221: train_A = "person images folder name" > >222: filenames_5 = "clothes images folder name" > >224: out_root_dir = "output folder name" > >225: origin_dir = "save input person images" > >226: target_dir = "save target clothes images" > >227: output_dir = "save output images" > >228: mask_dir = "save output masks" > >230: testing_number = "how much data you want to test" ## Step2: Segmentation ## <img height="200" src="https://github.com/b01902041/Deep-Virtual-Try-on-with-Clothes-Transform/blob/master/readme_img/segmentation.png"> ### code ### https://github.com/Engineering-Course/LIP_SSL * Modify mask: `modify_mask.m` * Save the masks file to png file: `show.m` * Combine all the masks: `combine_with_CAGANmask.m` ## Step3: Transform ## <img height="100" src="https://github.com/b01902041/Deep-Virtual-Try-on-with-Clothes-Transform/blob/master/readme_img/warping.png"> ### code and data ### * Training: `unet.py` `data.py` ``` python unet.py ``` * Testing: `Testing_unet.py` ``` python Testing_unet.py ``` * Data: `transform_data.zip` `transform_test_data.zip` ### parameters in code ### #### Training: unet.py >336: model_dir = "save model path" > >337: result_dir = "save results path" > >223: set "loss type" #### data.py >15: set "data path" #### Testing: Testing_unet.py >16: test_data_path = "data path" > >17: test_img_folder = "target clothes image folder name" > >18: test_mask_folder = "mask folder name" > >19: model_name = "model name" > >20: result_dir = "save results path" ## Step4: Combination ## <img height="200" src="https://github.com/b01902041/Deep-Virtual-Try-on-with-Clothes-Transform/blob/master/readme_img/combine.png"> ### code ### `Combine_image.m` ## Results <img src="https://github.com/b01902041/Deep-Virtual-Try-on-with-Clothes-Transform/blob/master/readme_img/result1.png"> <img src="https://github.com/b01902041/Deep-Virtual-Try-on-with-Clothes-Transform/blob/master/readme_img/result2.png"> <img src="https://github.com/b01902041/Deep-Virtual-Try-on-with-Clothes-Transform/blob/master/readme_img/result3.png"> <img src="https://github.com/b01902041/Deep-Virtual-Try-on-with-Clothes-Transform/blob/master/readme_img/condition.png">
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2ce769ca0a496f19, llm:Description: 'An image-based virtual try-on system with deep learning.' Language: Python. Repository name suggests virtual try-on with clothes transform (computer vision for fashion).
matched fp:2ce769ca0a496f19, llm:Description: 'An image-based virtual try-on system with deep learning.' Language: Python. Repository name suggests virtual try-on with clothes transform (computer vision for fashion).