Awesome

Accessory-Try-On-Dataset-STRAT

This is the dataset used in the following research papers:

• Spatial Transformation for Image Composition via Correspondence Learning [arXiv]
• Virtual Accessory Try-On via Keypoint Hallucination [arXiv]

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] (access code: bcmi) or [Google Drive].

Bibtex

If you use our dataset, please cite our paper using the following BibTeX [arxiv]:

@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},
}