# Fourier-CPPNs for Image Synthesis

arXiv: https://arxiv.org/abs/1909.09273 · HTML: https://mlsanity.com/paper/1909.09273/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [PREF: Phasorial Embedding Fields for Compact Neural Representations](https://mlsanity.com/paper/2205.13524/) (2022)
- [MISS GAN: A Multi-IlluStrator Style Generative Adversarial Network for image to illustration translation](https://mlsanity.com/paper/2108.05693/) (2021)
- [Image-based material analysis of ancient historical documents](https://mlsanity.com/paper/2203.01042/) (2022)

## Abstract (arXiv)

Compositional Pattern Producing Networks (CPPNs) are differentiable networks that independently map (x, y) pixel coordinates to (r, g, b) colour values. Recently, CPPNs have been used for creating interesting imagery for creative purposes, e.g., neural art. However their architecture biases generated images to be overly smooth, lacking high-frequency detail. In this work, we extend CPPNs to explicitly model the frequency information for each pixel output, capturing frequencies beyond the DC component. We show that our Fourier-CPPNs (F-CPPNs) provide improved visual detail for image synthesis.