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https://github.com/aimagelab/dress-code

Dress Code: High-Resolution Multi-Category Virtual Try-On. ECCV 2022
https://github.com/aimagelab/dress-code

artificial-intelligence computer-vision deep-learning dress-code eccv2022 virtual-try-on

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Dress Code: High-Resolution Multi-Category Virtual Try-On. ECCV 2022

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README 

        
# Dress Code Dataset



This repository presents the virtual try-on dataset proposed in:



*D. Morelli, M. Fincato, M. Cornia, F. Landi, F. Cesari, R. Cucchiara* 

**Dress Code: High-Resolution Multi-Category Virtual Try-On** 



**[[Paper](https://arxiv.org/abs/2204.08532)]** **[[Dataset Request Form](https://forms.gle/72Bpeh48P7zQimin7)]** **[[Try-On Demo](https://ailb-web.ing.unimore.it/dress-code)]**



**IMPORTANT!**

- By making any use of the Dress Code Dataset, you accept and agree to comply with the terms and conditions reported [here](https://github.com/aimagelab/dress-code/blob/main/LICENCE).

- The dataset will not be released to private companies. 

- When filling the dataset request form, non-institutional emails (e.g. gmail.com, qq.com, etc.) are not allowed.

- The signed release agreement form is mandatory (see the dataset request form for more details). Incomplete or unsigned release agreement forms are not accepted and will not receive a response. Typed signatures are not allowed.



**Requests are manually validated on a weekly basis. If you do not receive a response, your request does not meet the outlined requirements.**






Please cite with the following BibTeX:



```

@inproceedings{morelli2022dresscode,

  title={{Dress Code: High-Resolution Multi-Category Virtual Try-On}},

  author={Morelli, Davide and Fincato, Matteo and Cornia, Marcella and Landi, Federico and Cesari, Fabio and Cucchiara, Rita},

  booktitle={Proceedings of the European Conference on Computer Vision},

  year={2022}

}

```





    




## Dataset



We collected a new dataset for image-based virtual try-on composed of image pairs coming from different catalogs of YOOX NET-A-PORTER. 

The dataset contains more than 50k high resolution model clothing images pairs divided into three different categories (*i.e.* dresses, upper-body clothes, lower-body clothes).





    




### Summary

- 53792 garments

- 107584 images

- 3 categories

  - upper body

  - lower body

  - dresses

- 1024 x 768 image resolution

- additional info

  - keypoints

  - skeletons

  - human label maps

  - human dense poses



### Additional Info

Along with model and garment image pair, we provide also the keypoints, skeleton, human label map, and dense pose. 





  




More info



### Keypoints

For all image pairs of the dataset, we stored the joint coordinates of human poses.

In particular, we used [OpenPose](https://github.com/Hzzone/pytorch-openpose) [1] to extract 18 keypoints for each human body. 



For each image, we provided a json file containing a dictionary with the `keypoints` key.

The value of this key is a list of 18 elements, representing the joints of the human body. Each element is a list of 4 values, where the first two indicate the coordinates on the x and y axis respectively.



### Skeletons

Skeletons are RGB images obtained connecting keypoints with lines.



### Human Label Map



We employed a human parser to assign each pixel of the image to a specific category thus obtaining a segmentation mask for each target model. 

Specifically, we used the [SCHP model](https://github.com/PeikeLi/Self-Correction-Human-Parsing) [2] trained on the ATR dataset, a large single person human parsing dataset focused on fashion images with 18 classes.



Obtained images are composed of 1 channel filled with the category label value. 

Categories are mapped as follows:



```ruby

 0    background

 1    hat

 2    hair

 3    sunglasses

 4    upper_clothes

 5    skirt

 6    pants

 7    dress

 8    belt

 9    left_shoe

10    right_shoe

11    head

12    left_leg

13    right_leg

14    left_arm

15    right_arm

16    bag

17    scarf

```



### Human Dense Pose



We also extracted dense label and UV mapping from all the model images using [DensePose](https://github.com/facebookresearch/detectron2/tree/main/projects/DensePose) [3].



## Experimental Results



### Low Resolution 256 x 192



  

    Name

    SSIM

    FID

    KID

  

    

      CP-VTON [4]

      0.803

      35.16

      2.245

    

  

    

      CP-VTON+ [5]

      0.902

      25.19

      1.586

    

  

    

      CP-VTON* [4]

      0.874

      18.99

      1.117

    

  

    

      PFAFN [6]

      0.902

      14.38

      0.743

    

  

    

      VITON-GT [7]

      0.899

      13.80

      0.711

    

  

    

      WUTON [8]

      0.902

      13.28

      0.771

    

  

    

      ACGPN [9]

      0.868

      13.79

      0.818

    

  

    

      OURS

      0.906

      11.40

      0.570

    

  



## Code

Due to a firm collaboration, we cannot release the code. However, we supply an empty Pytorch project to load data.

## References



[1] Cao, et al. "OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields." IEEE TPAMI, 2019.



[2] Li, et al. "Self-Correction for Human Parsing." arXiv, 2019.



[3] Güler, et al. "Densepose: Dense human pose estimation in the wild." CVPR, 2018.



[4] Wang, et al. "Toward Characteristic-Preserving Image-based Virtual Try-On Network." ECCV, 2018.



[5] Minar, et al. "CP-VTON+: Clothing Shape and Texture Preserving Image-Based Virtual Try-On." CVPR Workshops, 2020.



[6] Ge, et al. "Parser-Free Virtual Try-On via Distilling Appearance Flows." CVPR, 2021.



[7] Fincato, et al. "VITON-GT: An Image-based Virtual Try-On Model with Geometric Transformations." ICPR, 2020.



[8] Issenhuth, el al. "Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On." ECCV, 2020.



[9] Yang, et al. "Towards Photo-Realistic Virtual Try-On by Adaptively Generating-Preserving Image Content." CVPR, 2020.



## Contact



If you have any general doubt about our dataset, please use the [public issues section](https://github.com/aimagelab/dress-code/issues) on this github repo. Alternatively, drop us an e-mail at davide.morelli [at] unimore.it or marcella.cornia [at] unimore.it.