dress-code

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] [Dataset Request Form] [Try-On Demo]

IMPORTANT!

• By making any use of the Dress Code Dataset, you accept and agree to comply with the terms and conditions reported here.
• 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.

 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

Experimental Results

Low Resolution 256 x 192

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 on this github repo. Alternatively, drop us an e-mail at davide.morelli [at] unimore.it or marcella.cornia [at] unimore.it.