# Learnable Fractal Flames

arXiv: https://arxiv.org/abs/2406.09328 · HTML: https://mlsanity.com/paper/2406.09328/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning](https://mlsanity.com/paper/2511.17731/) (2025)

## Abstract (arXiv)

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.