New paper on neural painting "Stylized Neural Painting".
The main idea is to train a neural network to render individual brushstrokes, parametrized by color, shape, and transparency.
An input image is approximated by a fixed number of brushstrokes which are blended based on their transparency values.
To find optimal parameters for each brushstroke authors propose to run an iterative optimization procedure in the same way as it was done in the pioneering work of Gatys et al.

Another novelty of this paper is Optimal Transport loss, which has more meaningful gradients compared to the photometric loss in case of sparse brushstrokes.

Authors even created a google Colab notebook, where you can play around with the method.

📃 https://arxiv.org/pdf/2011.08114.pdf
🌐 https://jiupinjia.github.io/neuralpainter/
💾 https://github.com/jiupinjia/stylized-neural-painting

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New paper on neural painting "Stylized Neural Painting".
The main idea is to train a neural network to render individual brushstrokes, parametrized by color, shape, and transparency.
An input image is approximated by a fixed number of brushstrokes which are blended based on their transparency values.
To find optimal parameters for each brushstroke authors propose to run an iterative optimization procedure in the same way as it was done in the pioneering work of Gatys et al.

Another novelty of this paper is Optimal Transport loss, which has more meaningful gradients compared to the photometric loss in case of sparse brushstrokes.

Authors even created a google Colab notebook, where you can play around with the method.

📃 https://arxiv.org/pdf/2011.08114.pdf
🌐 https://jiupinjia.github.io/neuralpainter/
💾 https://github.com/jiupinjia/stylized-neural-painting

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1.1K viewsedited  Nov 18, 2020 at 05:30