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Fourier-CPPNs for Image Synthesis

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.

Publication
Workshop at the International Conference on Computer Vision (ICCV)
David Vazquez
David Vazquez
Manager of Research Programs

Manager of Research Programs at Research Management located at Montreal, QC, Canada.