Style-Based Global Appearance Flow for Virtual Try-On (CVPR 2022)

Description

Awesome try-on desplays are like this:

Author: @FerryHuang.

This is an implementation of https://github.com/SenHe/Flow-Style-VTON adapting to mmediting. Only inference is supported so far.

Usage

Setup Environment

Please refer to Get Started to install MMEditing.

At first, add the current folder to PYTHONPATH, so that Python can find your code. Run command in the current directory to add it.

Please run it every time after you opened a new shell.

export PYTHONPATH=`pwd`:$PYTHONPATH

Data Preparation

Please check the official repo and download test-set and pretrained checkpoints and put them under the folder projects/flow_style_vton

Testing commands

To test with single GPU:

cd projects/flow_style_vton
python inference.py

Expectedly, two folders will be made im_gar_flow_wg and our_t_results, containing the try-on procedures and the final results, respectively.

Citation

@inproceedings{he2022fs_vton,
  title={Style-Based Global Appearance Flow for Virtual Try-On},
  author={He, Sen and Song, Yi-Zhe and Xiang, Tao},
  booktitle={CVPR},
  year={2022}
}

Checklist [required]

Here is a checklist of this project's progress. And you can ignore this part if you don't plan to contribute to MMediting projects.

• Milestone 1: PR-ready, and acceptable to be one of the projects/.

  • Finish the code

  • Basic docstrings & proper citation

  • Converted checkpoint and results (Only for reproduction)

• Milestone 2: Indicates a successful model implementation.

  • Training results

• Milestone 3: Good to be a part of our core package!

  • Unit tests

  • Code style

  • metafile.yml and README.md