Awesome

FIFA ⚽

This is official code for our BMVC 2022 paper:<br> Fill in Fabrics: Body-Aware Self-Supervised Inpainting for Image-Based Virtual Try-On <br>

attention

1. Specification of dependencies

This code requires Python 3.8.12. Run commands below to make your environment.

conda create --name fifa python=3.8
conda activate fifa
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c nvidia
conda install -c conda-forge jupyterlab
pip install opencv-python, matplotlib, sklearn, tqdm, pycocotools, tensorboard, PyWavelets, tensorboardX

Or, do below to install the required packages.

conda update conda
conda env create -f environment.yml
conda activate fifa

2a. Training code

To train on VITON dataset, you will need to download vgg19-dcbb9e9d.pth and keep it inside the folder train_src/models/.

To train FIFA on VITON dataset, download the VITON training and test datasets from here. Make a folder named datasets/acgpn_data and put them there. Data directory tree will look like:

datasets/
    acgpn_data/
        try_on_training/
        try_on_testing/

VITON dataset is presented in VITON, containing 19,000 image pairs, each of which includes a front-view woman image and a top clothing image. After removing the invalid image pairs, it yields 16,253 pairs, further splitting into a training set of 14,221 paris and a testing set of 2,032 pairs.

Now, from your terminal, run the following to train FIFA in two stages. First, the Fabricator is trained using:

python train_fabricator.py --name fabricator

Then, VTON pipeline is trained using

python train.py --name fifa

A Colab training notebook is available with a subset of the dataset, see notebook.

After training, see checkpoints folder for the model weights.

All experiments are conducted on a single NVIDIA 3080Ti GPU. For additional implementation details and results, please refer to the supplementary material here.

2b. Evaluation code

To evaluate the performance of FIFA on the VITON test set, run test.ipynb inside the folder test_src/notebooks which shows visualizations as well as SSIM and FID scores.

SSIM and FID scores computed using pytorch-ssim and pytorch-fid.

3. Pre-trained models

Pre-trained models are available in GitHub Releases.

4. Demo

A hugging face spaces and colab demo is available is this repository here.

5. Citation

@inproceedings{zunair2022fill,
  title={Fill in Fabrics: Body-Aware Self-Supervised Inpainting for Image-Based Virtual Try-On},
  author={Zunair, Hasib and Gobeil, Yan and Mercier, Samuel and Hamza, A Ben},
  journal={Proc. British Machine Vision Conference},
  year={2022}

Future works

VITON-HD dataset was recently released and use by HR-VITON. One direction is to build Decathlon VTON dataset in the format of VITON-HD.
Our approach uses human parsing (e.g. semantic segmentation) during inference only, a very recent method Flow-Style-VTON eliminates this step for faster inference.
A possible approach to improve the method is to train the model with person images without background.

Acknowledgements

This work is built on top of CVPR 2020 paper Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content (Paper, Code). Thanks to the authors!