Image Style Transfer Using Convolutional Neural Networks

Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from sty...

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Bibliographic Details
Published in:2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 2414 - 2423
Main Authors: Gatys, Leon A., Ecker, Alexander S., Bethge, Matthias
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2016
Subjects:
Feature extraction
Image reconstruction
Image representation
Neural networks
Neuroscience
Semantics
Visualization
ISSN:1063-6919
Online Access:Get full text
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Summary:Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.
ISSN:1063-6919
DOI:10.1109/CVPR.2016.265