Fourier-CPPNs for Image Synthesis

Mattie Tesfaldet, Xavier Snelgrove, David Vazquez; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 0-0

Abstract


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.

Related Material


[pdf]
[bibtex]
@InProceedings{Tesfaldet_2019_ICCV,
author = {Tesfaldet, Mattie and Snelgrove, Xavier and Vazquez, David},
title = {Fourier-CPPNs for Image Synthesis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}
}