Dress Code Dataset
D. Morelli, M. Fincato, M. Cornia, F. Landi, F. Cesari, R. Cucchiara
Dress Code: High-Resolution Multi-Category Virtual Try-On
European Conference on Computer Vision (ECCV)
By making any use of the Dress Code Dataset, you accept and agree to comply with the terms and conditions reported [here].
Please cite with the following BibTeX:
We collected a new dataset for image-based virtual try-on composed of image pairs coming from different catalogs of YOOX NET-A-PORTER Group.
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).
- 53792 garments
- 107584 images
- 3 categories
- upper body
- lower body
- 1024 x 768 image resolution
- additional infos
Along with model and garment pair image, we provide also the keypoints, skeletons, image label map, and densePose.
For all image pairs of the dataset, we stored the joint coordinates of human poses. In particular, we used OpenPose  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 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  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:
Human Dense Pose
We also extracted dense label and UV mapping from all the model images using DensePose .
 Cao, et al. "OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields." IEEE TPAMI, 2019.
 Li, et al. "Self-Correction for Human Parsing." arXiv, 2019.
 Güler, et al. "Densepose: Dense human pose estimation in the wild." CVPR, 2018.
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.