Son of Zorn's lemma: Targeted style transfer using instance-aware semantic segmentation

Style transfer is an important task in which the style of a source image is mapped onto that of a target image. The method is useful for synthesizing derivative works of a particular artist or specific painting. This work considers targeted style transfer, in which the style of a template image is u...

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Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1348 - 1352
Main Authors: Castillo, Carlos, Soham De, Xintong Han, Singh, Bharat, Yadav, Abhay Kumar, Goldstein, Tom
Format: Conference Proceeding
Language:English
Published: IEEE 01.03.2017
Subjects:
Computational modeling
Convolution neural network
Feature extraction
Image color analysis
Image filtering
Image segmentation
Instance-aware semantic segmentation
Markov processes
Markov random fields
Neural networks
Semantics
Style transfer
ISSN:2379-190X
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Abstract Style transfer is an important task in which the style of a source image is mapped onto that of a target image. The method is useful for synthesizing derivative works of a particular artist or specific painting. This work considers targeted style transfer, in which the style of a template image is used to alter only part of a target image. For example, an artist may wish to alter the style of only one particular object in a target image without altering the object's general morphology or surroundings. This is useful, for example, in augmented reality applications (such as the recently released Pokémon go), where one wants to alter the appearance of a single real-world object in an image frame to make it appear as a cartoon. Most notably, the rendering of real-world objects into cartoon characters has been used in a number of films and television show, such as the upcoming series Son of Zorn. We present a method for targeted style transfer that simultaneously segments and stylizes single objects selected by the user. The method uses a Markov random field model to smooth and anti-alias outlier pixels near object boundaries, so that stylized objects naturally blend into their surroundings.
AbstractList Style transfer is an important task in which the style of a source image is mapped onto that of a target image. The method is useful for synthesizing derivative works of a particular artist or specific painting. This work considers targeted style transfer, in which the style of a template image is used to alter only part of a target image. For example, an artist may wish to alter the style of only one particular object in a target image without altering the object's general morphology or surroundings. This is useful, for example, in augmented reality applications (such as the recently released Pokémon go), where one wants to alter the appearance of a single real-world object in an image frame to make it appear as a cartoon. Most notably, the rendering of real-world objects into cartoon characters has been used in a number of films and television show, such as the upcoming series Son of Zorn. We present a method for targeted style transfer that simultaneously segments and stylizes single objects selected by the user. The method uses a Markov random field model to smooth and anti-alias outlier pixels near object boundaries, so that stylized objects naturally blend into their surroundings.
Author Castillo, Carlos
Goldstein, Tom
Xintong Han
Yadav, Abhay Kumar
Soham De
Singh, Bharat
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  fullname: Goldstein, Tom
  organization: Dept. of Comput. Sci., Univ. of Maryland, College Park, MD, USA
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Snippet Style transfer is an important task in which the style of a source image is mapped onto that of a target image. The method is useful for synthesizing...
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StartPage 1348
SubjectTerms Computational modeling
Convolution neural network
Feature extraction
Image color analysis
Image filtering
Image segmentation
Instance-aware semantic segmentation
Markov processes
Markov random fields
Neural networks
Semantics
Style transfer
Title Son of Zorn's lemma: Targeted style transfer using instance-aware semantic segmentation
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