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arXiv · 2406.09328

Learnable Fractal Flames

Jordan J. Bannister·Derek Nowrouzezahrai
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Abstract

This work presents a differentiable rendering approach that allows latent fractal flame parameters to be learned from image supervision using gradient descent optimization. The approach extends the state-of-the-art in differentiable iterated function system fractal rendering through support for color images, non-linear generator functions, and multi-fractal compositions. With this approach, artists can use reference images to quickly and intuitively control the creation of fractals. We describe the approach and conduct a series of experiments exploring its use, culminating in the creation of complex and colorful fractal artwork based on famous paintings.

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BibTeXRIS

Jordan J. Bannister, Derek Nowrouzezahrai. 2024-06-13. Learnable Fractal Flames. https://arxiv.org/abs/2406.09328

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