Impressive Adobe Algorithm Transfers One Photo's Style Onto Another
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
Convolutional neural networks (CNNs) have proven highly effective at image synthesis and style transfer. For most users, however, using them as tools can be a challenging task due to their unpredictable behavior that goes against common intuitions. This paper introduces a novel concept to augment such generative architectures with semantic annotations, either by manually authoring pixel labels or
Deep Photo Style Transfer
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. Our approach builds upon the recent work on painterly transfer that separates style from the content of an image by considering different layers of a neural network. However, as is, this approach is not suitable for photoreal
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a \emph{per-pixel} loss between the output and ground-truth images. Parallel work has shown that high-quality images can be generated by defining and optimizing \emph{perceptual} loss functions based o
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Fast color transfer from multiple images
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.524.6154&rep=rep1&type=pdf
Image Style Transfer Using Convolutional Neural Networks
Color transfer between images - Computer Graphics and Applications, IEEE
