metadata

title: White-box Style Transfer Editing (WISE)
emoji: 🎨
colorFrom: pink
colorTo: red
sdk: streamlit
sdk_version: 1.10.0
app_file: Whitebox_style_transfer.py
tags:
  - Style Transfer
  - Image Synthesis
  - Editing
  - Painting
pinned: false
license: mit

White-box Style Transfer Editing (WISE) Demo

This app demonstrates the editing capabilities of the White-box Style Transfer Editing (WISE) framework. It optimizes the parameters of classical image processing filters to match a given style image. After optimization, parameters can be tuned by hand to achieve a desired look.

How does it work?

We provide a small stylization effect that contains several filters such as bump mapping or edge enhancement that can be optimized. The optimization yields so-called parameter masks, which contain per-pixel parameter settings for each filter.

🚀 Try it out 🚀

Our demo is now on huggingface: huggingface/Whitebox-Style-Transfer-Editing

To run locally, clone the repo recursively and install the dependencies in requirements.txt. Set HUGGINGFACE to false in demo_config.py. Then run the streamlit app using streamlit run Whitebox_style_transfer.py

Links & Paper

Project page, arxiv link, framework code

"WISE: Whitebox Image Stylization by Example-based Learning", by Winfried Lötzsch*, Max Reimann*, Martin Büßemeyer, Amir Semmo, Jürgen Döllner, Matthias Trapp, in ECCV 2022

Further notes

Pull Requests and further improvements welcome. Please note that the shown effect is a minimal pipeline in terms of stylization capability, the much more feature-rich oilpaint and watercolor pipelines we show in our ECCV paper cannot be open-sourced due to IP reasons.

@misc{loetzsch2022wise,
    title={WISE: Whitebox Image Stylization by Example-based Learning},
    author={Lötzsch, Winfried and Reimann, Max and Büssemeyer, Martin and Semmo, Amir and Döllner, Jürgen and Trapp, Matthias},
    year={2022},
    eprint={2207.14606},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}