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https://github.com/bcmi/Accessory-Try-On-Dataset-STRAT
A virtual accessory try-on dataset which could be used for image composition
https://github.com/bcmi/Accessory-Try-On-Dataset-STRAT
image-composition virtual-try-on
Last synced: 3 months ago
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A virtual accessory try-on dataset which could be used for image composition
- Host: GitHub
- URL: https://github.com/bcmi/Accessory-Try-On-Dataset-STRAT
- Owner: bcmi
- License: mit
- Created: 2023-08-19T12:37:08.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-14T14:11:05.000Z (9 months ago)
- Last Synced: 2024-04-04T22:13:51.392Z (7 months ago)
- Topics: image-composition, virtual-try-on
- Language: Python
- Homepage:
- Size: 271 KB
- Stars: 6
- Watchers: 5
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Accessory-Try-On-Dataset-STRAT
This is the dataset used in the following research papers:
* Spatial Transformation for Image Composition via Correspondence Learning [[arXiv]](https://arxiv.org/pdf/2207.02398.pdf)
* Virtual Accessory Try-On via Keypoint Hallucination [[arXiv]](https://arxiv.org/pdf/2310.17131.pdf)
## Dataset
Our STRAT dataset contains three subdatasets: STRAT-glasses, STRAT-hat, and STRAT-tie, which correspond to "glasses try-on", "hat try-on", and "tie try-on" respectively. By treating accessory image (*resp.*, human face or portrait image) as foreground (*resp.*, background), we ask human annotators to paste the foreground on the background and adjust four vertices of foreground to generate a realistic composite image. After adjustment, the coordinates of four vertices are recorded. In each subdataset, the training set has **2000** pairs of foregrounds and backgrounds, while the test set has **1000** pairs of foregrounds and backgrounds. Some examples of foreground, background, and annotated composite image with composite foreground mask from three subdatasets are show in the figure below.
## Annotation
In the ``trans_params.json``, we record both the vertice coordinates and warpping parameters of the foreground for each pairwise sample. Specifically, ``init_vertex`` represents the four vertices of the accessory in foreground image, and ``final_vertex`` means the four vertices of the warped accessory in composite image. Given pairwise ``init_vertex`` and ``final_vertex``, the transformation matrix (``trans_matri``) can be derived by calling ``findHomography`` function of OpenCV library. To facilitate verification of the warpping parameters we provide or your own predicted parameters, we shared python scripts ``composite_by_opencv.py`` and ``composite_by_pytorch.py`` in this repository, which implement vertex transformation and composite image generation based on transformation matrix using opencv and pytorch, respectively. Considering that recent virtual try-on methods usually require semantic parsing, we also provide ground-truth semantic annotations in ``background_mask`` and ``background_mask_comp`` folders.
## Download
The STRAT dataset is provided in [[Baidu Cloud]](https://pan.baidu.com/s/13J7jyxexNausxwEvVgaAfg) (access code: bcmi) or [[Google Drive]](https://drive.google.com/file/d/1vHN1vvg2JQZKRfqrYfQjyuJNvgkP9-gY/view?usp=sharing).
## Bibtex
If you use our dataset, please cite our paper using the following BibTeX [[arxiv](https://arxiv.org/pdf/2207.02398.pdf)]:
```
@article{STRAT,
author = {Bo Zhang and
Yue Liu and
Kaixin Lu and
Li Niu and
Liqing Zhang},
title = {Spatial Transformation for Image Composition via Correspondence Learning},
journal = {CoRR},
volume = {abs/2207.02398},
year = {2022},
}
```