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.
Cite
@article{arxiv.1909.09273,
title = {Fourier-CPPNs for Image Synthesis},
author = {Mattie Tesfaldet and Xavier Snelgrove and David Vazquez},
journal= {arXiv preprint arXiv:1909.09273},
year = {2019}
}