Controlling Perceptual Factors in Neural Style Transfer
Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann, Eli Shechtman
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/leongatys/NeuralImageSynthesisOfficialIn papertorch★ 0
- github.com/Garfield35/Doodletf★ 0
- github.com/ProGamerGov/neural-style-ptpytorch★ 0
- github.com/cal-app/slow-NSTtf★ 0
- github.com/leongatys/PytorchNeuralStyleTransferpytorch★ 0
- github.com/dstein64/pastichepytorch★ 0
Abstract
Neural Style Transfer has shown very exciting results enabling new forms of image manipulation. Here we extend the existing method to introduce control over spatial location, colour information and across spatial scale. We demonstrate how this enhances the method by allowing high-resolution controlled stylisation and helps to alleviate common failure cases such as applying ground textures to sky regions. Furthermore, by decomposing style into these perceptual factors we enable the combination of style information from multiple sources to generate new, perceptually appealing styles from existing ones. We also describe how these methods can be used to more efficiently produce large size, high-quality stylisation. Finally we show how the introduced control measures can be applied in recent methods for Fast Neural Style Transfer.