Learnable Fractal Flames
Jordan J. Bannister, Derek Nowrouzezahrai

TL;DR
This paper introduces a differentiable rendering method for fractal flames that enables learning of fractal parameters from images, supporting color, non-linear functions, and multi-fractals, facilitating artistic control and complex artwork creation.
Contribution
It extends differentiable fractal rendering to include color, non-linear functions, and multi-fractals, allowing intuitive image-based control of fractal generation.
Findings
Enables learning fractal parameters from images using gradient descent.
Supports complex, colorful fractal artwork creation.
Provides a new tool for artists to control fractal design via reference images.
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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Taxonomy
TopicsAnomaly Detection Techniques and Applications
