Max Reimann
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README.md
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license: mit
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---
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license: mit
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---
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# White-box Style Transfer Editing (WISE) Demo
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This app demonstrates the editing capabilities of the [White-box Style Transfer Editing (WISE) framework](https://github.com/winfried-ripken/wise).
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It optimizes the parameters of classical image processing filters to match a given style image.
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After optimization, parameters can be tuned by hand to achieve a desired look.
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### How does it work?
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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 of each filter.
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## 🚀 Try it out 🚀
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**Our demo is now on huggingface: [huggingface/Whitebox-Style-Transfer-Editing](https://huggingface.co/spaces/MaxReimann/Whitebox-Style-Transfer-Editing)**
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![Streamlit Screenshot](images/screen_wise_demo.jpg?raw=true "WISE Editing Demo")
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To run it **locally**, clone the repo recursively and install the dependencies in requirements.txt. Set HUGGINGFACE to false in demo_config.py.
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Then run the streamlit app using `streamlit run Whitebox_style_transfer.py`
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## Links & Paper
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[Project page](https://ivpg.hpi3d.de/wise/),
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[arxiv link](https://arxiv.org/abs/2207.14606),
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[framework code](https://github.com/winfried-ripken/wise)
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"WISE: Whitebox Image Stylization by Example-based Learning", by Winfried Lötzsch*, Max Reimann*, Martin Büßemeyer, Amir Semmo, Jürgen Döllner, Matthias Trapp, in ECCV 2022
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### Further notes
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Pull Requests and further improvements welcome.
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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.
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images/screen_wise_demo.jpg
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### Links & Paper
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[Project page](https://ivpg.hpi3d.de/wise/),
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[arxiv link](https://arxiv.org/abs/2207.14606)
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"WISE: Whitebox Image Stylization by Example-based Learning", by Winfried Lötzsch*, Max Reimann*, Martin Büßemeyer, Amir Semmo, Jürgen Döllner, Matthias Trapp, in ECCV 2022
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### Links & Paper
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[Project page](https://ivpg.hpi3d.de/wise/),
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[arxiv link](https://arxiv.org/abs/2207.14606)
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[demo code](https://github.com/MaxReimann/WISE-Editing)
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"WISE: Whitebox Image Stylization by Example-based Learning", by Winfried Lötzsch*, Max Reimann*, Martin Büßemeyer, Amir Semmo, Jürgen Döllner, Matthias Trapp, in ECCV 2022
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