Unifying Color and Texture Transfer for Predictive Appearance Manipulation

Recent color transfer methods use local information to learn the transformation from a source to an exemplar image, and then transfer this appearance change to a target image. These solutions achieve very successful results for general mood changes, e.g., changing the appearance of an image from “su...

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Veröffentlicht in:Computer graphics forum Jg. 34; H. 4; S. 53 - 63
Hauptverfasser: Okura, Fumio, Vanhoey, Kenneth, Bousseau, Adrien, Efros, Alexei A., Drettakis, George
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.07.2015
Wiley
Schlagworte:
Algorithms
and texture
Categories and Subject Descriptors (according to ACM CCS)
Color
Computer graphics
Computer Science
Computer Vision and Pattern Recognition
Customization
I.3.3 [Computer Graphics]: Picture/Image Generation
I.3.7 [Computer Graphics]: Color
I.3.7 [Computer Graphics]: Color, shading, shadowing, and texture
Leaves
Mathematical models
shading
shadowing
Studies
Surface layer
Texture
Trees
United States > US
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Recent color transfer methods use local information to learn the transformation from a source to an exemplar image, and then transfer this appearance change to a target image. These solutions achieve very successful results for general mood changes, e.g., changing the appearance of an image from “sunny” to “overcast”. However, such methods have a hard time creating new image content, such as leaves on a bare tree. Texture transfer, on the other hand, can synthesize such content but tends to destroy image structure. We propose the first algorithm that unifies color and texture transfer, outperforming both by leveraging their respective strengths. A key novelty in our approach resides in teasing apart appearance changes that can be modeled simply as changes in color versus those that require new image content to be generated. Our method starts with an analysis phase which evaluates the success of color transfer by comparing the exemplar with the source. This analysis then drives a selective, iterative texture transfer algorithm that simultaneously predicts the success of color transfer on the target and synthesizes new content where needed. We demonstrate our unified algorithm by transferring large temporal changes between photographs, such as change of season – e.g., leaves on bare trees or piles of snow on a street – and flooding.
Bibliographie:ark:/67375/WNG-2401DJNX-G
istex:3C4112E190EC87886947095CA391EA56D30C6282
ArticleID:CGF12678
Supporting Information
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12678