You need to agree to share your contact information to access this dataset
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
The IGPair is available for non-commercial research purposes only. You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit any portion of the images and any portion of the derived data for commercial purposes. You agree NOT to further copy, publish or distribute any portion of IGPair to any third party for any purpose. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset. IMAG Team reserves the right to terminate your access to the IGPair at any time.
Log in or Sign Up to review the conditions and access this dataset content.
IGPair Dataset
Overview
IGPair includes multiple models for each clothing item. It is also the first dataset with a resolution exceeding 2k*2k. Additionally, IGPair is the first publicly available dataset that includes textual descriptions, diverse scenes, and various styles. Specifically, IGPair includes 86,873 garments. We categorize the garment into 18 types, and the dataset consists of 324,857 image pairs.
Data Structure
Now we release the body mask (in folder './body_mask/'), clothes (in folder './clothes/'), densepose (in folder './densepose/'), openpose (in folder './openpose/'), IGPair Test Data (in folder './IGPair_Test/') and annotations (in folder './IGPair.json/'), for IGPair.
Each annotation adheres to the subsequent Parquet format specifications, including column names and corresponding content examples:
{
"text": "caption",
"image_file": "model_path",
"cloth_file": "cloth_path",
"cloth_type": "type"
}
Type Counter
Test Set
The test set includes 1600 upper-body cloth, 200 dress, and 200 lower-body cloth images in total. The image naming format is upper_body_00001.jpg, dress_01601.jpg, and lower_body_01801.jpg.
Download Instructions
cd IGPair/IGPair_dataset
cat IGPair.zip.* > IGPair.zip
Citation Information
@article{shen2024imagdressing,
title={IMAGDressing-v1: Customizable Virtual Dressing},
author={Shen, Fei and Jiang, Xin and He, Xin and Ye, Hu and Wang, Cong and Du, Xiaoyu and Li, Zechao and Tang, Jinhui},
journal={arXiv preprint arXiv:2407.12705},
year={2024}
}
Contributors
Shen Fei and Jiang Xin
- Downloads last month
- 6