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