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Street TryOn Dataset/Benchmark

(WACV'25)

StreetTryOn, the new in-the-wild Virtual Try-On dataset, consists of 12, 364 and 2, 089 street person images for training and validation, respectively. It is derived from the large fashion retrieval dataset DeepFashion2, from which we filter out over 90% of DeepFashion2 images that are infeasible for try-on tasks (e.g., non-frontal view, large occlusion, dark environment, etc.). Combining with the garment and person images in VITON-HD, we obtain a comprehensive suite of in-domain and cross-domain try-on tasks that have garment and person inputs from various sources, including Shop2Model, Model2Model, Shop2Street, and Street2Street.

This is the official release of the StreetTryOn Dataset and more details can be found in our paper Street TryOn: Learning In-the-wild Virtual Try-On from Unpaired Person Images.

Five Virtual Try-On tasks are covered:

In-domain Tasks: Shop2Model Test, Model2Model Test, Street2Street Test (New)
Cross-domain Tasks: Shop2Street Test (New), Model2Street Test (New)

Street TryOn Dataset

Street TryOn Dataset contains unpaired in-the-wild person images that can be used for virtual try-on tasks. Street TryOn Dataset consists of 12,364 and 2089 images filtered from Deepfashion2 Dataset for training and validation.

We release all the annotations mentioned in our paper. Note for images: we provide scripts to extract them from DeepFashion2 dataset. Please follow the below steps to download the dataset into your datapath $DATA.

Licenses

Since this dataset is derived from Deepfashion2 Dataset, the same license is inherited.

Downloading Steps

Obtain the data access of DeepFashion2 from its official release for a zip password which will be used later to unzip the images.
Clone this repo under $DATA by

git clone https://github.com/cuiaiyu/street-tryon-benchmark

Download the released Street TryOn Data annotations from this link and unzip it under $DATA as $DATA/street_tryon
Download, filter and process the images from DeepFasshion2 by running the below script

# The password obtained in Step 1 will be used here.
sh street-tryon-benchmark/get_street_images.sh

Move the tutorial notebook under $DATA

mv street-tryon-benchmark/street_tryon_tutorial.ipynb .

After that, you should have the data as:

- $DATA
    - street_tryon/
        - train/
            - image/
            - densepose/
            - raw_bbox/
            - ...
        - validation/
            - image/
            - densepose/
            - raw_bbox/
            - ...
        - annotations/
            - street2street_test_pairs_top.csv
            - street2street_test_pairs_dress.csv
            - ...
    - street-tryon-benchmark/
        - ...
    - street_tryon_tutorial.ipynb

Set up VITON-HD Dataset for Cross-domain Virtual Try-On Test

To run the cross-domain virtual try-on test, please also download the VITON-HD dataset from its official release and unzip it under $DATA. The $DATA directory should look like

- $DATA
    - street_tryon/
        - train/
            - ...
        - validation/
            - ...
        - annotations/
            - ...
    - zalando/ (VITON-HD)
        - train/
            - ...
        - test/
            - ...
    - street-tryon-benchmark/
        - ...
    - street_tryon_tutorial.ipynb

Additional annotations for VITON-HD

We also release the full DensePose annotations for both human and garment images in VITON-HD:

Human DensePose (Download link): obtained by the official model of detectron2-DensePose
Garment DensePose (Download link): obtained by the reimplemented model of this method by Cui et al. 2023

Please download the data and put them in zalando/train and zalando/test respectively.

If you find the additional annotation is helpful, please consider citing the original detection methods.

Load Data for Multiple Tests

We provide a PyTorch dataloader to load data from the same domain or cross domain flexibly.

After VITON-HD and street-tryon datasets are set up, one can run the following code to load the data for the proposed tests in the paper:

from street_tryon_benchmark.dataset import GeneralTryOnDataset


def get_dataset_by_task(task):
    if task == 'shop2model':
        config_path = "street_tryon_benchmark/configs/shop2model.yaml"
    elif task == 'shop2street':
        config_path = "street_tryon_benchmark/configs/shop2street.yaml"
    elif task == 'model2model':
        config_path = "street_tryon_benchmark/configs/model2model.yaml"
    elif task == 'model2street':
        config_path = "street_tryon_benchmark/configs/model2street.yaml"
    elif task == 'street2street-top':
        config_path = "street_tryon_benchmark/configs/street2street_top.yaml"
    elif task == 'street2street-dress':
        config_path = "street_tryon_benchmark/configs/street2street_dress.yaml"
    else:
        raise NotImplementedError


    with open(config_path, "r") as f:
        data_config = yaml.safe_load(f)

    return GeneralTryOnDataset(".", config=data_config, split='test')

# create dataset for street2street task
dataset = get_dataset_by_task('street2street-top')

# check data
curr = dataset[0]

# get person-related data
pimg, piuv, pseg = curr['pimg'], curr['piuv'], curr['pseg']

# get garment-related data
gimg, giuv, gseg = curr['gimg'], curr['giuv'], curr['gseg']

We also provide a notebook to play with this dataloader.

Note:

The default dataset configuration is loading ATR segmentation for street images and loading the provided segmentation for VITON-HD. If you need either in different format, you can change the segm_dir or garment_segm_dir in the corresponding .yaml config file with your new datapath.

Licenses

We inherit the licenses from both DeepFashion2 Dataset and VITON-HD. The usage of the data and code has to meet the requirements of both licenses.

No commerical usage is allowed.

Citations

If you find this work helpful, please cite us as:

@article{cui2023street-tryon,
  title={Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images},
  author={Cui, Aiyu and Mahajan, Jay and Shah, Viraj and Gomathinayagam, Preeti and Lazebnik, Svetlana},
  journal={arXiv preprint arXiv:2311.16094},
  year={2023}
}