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
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ISSN:0167-7055, 1467-8659
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Abstract 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.
AbstractList 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.
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.
Author Drettakis, George
Okura, Fumio
Vanhoey, Kenneth
Efros, Alexei A.
Bousseau, Adrien
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  surname: Okura
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  givenname: Kenneth
  surname: Vanhoey
  fullname: Vanhoey, Kenneth
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  givenname: Adrien
  surname: Bousseau
  fullname: Bousseau, Adrien
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  givenname: Alexei A.
  surname: Efros
  fullname: Efros, Alexei A.
  organization: University of California, Berkeley
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  givenname: George
  surname: Drettakis
  fullname: Drettakis, George
  organization: Inria
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Copyright 2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.
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Copyright_xml – notice: 2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.
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References_xml – reference: HaCohen Y., Shechtman E., Goldman D.B., Lischinski D.: Non-rigid dense correspondence with applications for image enhancement. ACM Transactions on Graphics (Proc. SIGGRAPH) 30, 4 (2011), 70.
– reference: Eisemann E., Durand F.: Flash photography enhancement via intrinsic relighting. ACM Transactions on Graphics (Proc. SIGGRAPH) 23 (2004).
– reference: Achanta R., Shaji A., Smith K., Lucchi A., Fua P., Susstrunk S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Analysis Machine Intelligence (PAMI) 34, 11 (Nov. 2012), 2274-2282.
– reference: Wei L.-Y., Lefebvre S., Kwatra V., Turk G.: State of the art in example-based texture synthesis. In Eurographics 2009, State of the Art Report, EG-STAR (2009), Eurographics Association.
– reference: Bae S., Paris S., Durand F.: Two-scale tone management for photographic look. ACM Transactions on Graphics (Proc. SIGGRAPH) 25, 3 (2006), 637-645.
– reference: Laffont P.-Y., Ren Z., Tao X., Qian C., Hays J.: Transient attributes for high-level understanding and editing of outdoor scenes. ACM Transactions on Graphics (Proc. SIGGRAPH) 33, 4 (2014).
– reference: Otsu N.: A Threshold Selection Method from Gray-level Histograms. IEEE Transactions on Systems, Man and Cybernetics 9, 1 (1979), 62-66.
– reference: Johnson M.K., Dale K., Avidan S., Pfister H., Freeman W.T., Matusik W.: Cg2real: Improving the realism of computer generated images using a large collection of photographs. Visualization and Computer Graphics, IEEE Transactions on 17, 9 (2011), 1273-1285.
– reference: Reinhard E., Ashikhmin M., Gooch B., Shirley P.: Color transfer between images. IEEE Computer graphics and applications 21, 5 (2001), 34-41.
– reference: Pitié F., Kokaram A.C., Dahyot R.: Automated colour grading using colour distribution transfer. Computer Vision and Image Understanding 107, 1 (2007), 123-137.
– reference: Tai Y.-W., Jia J., Tang C.-K.: Local color transfer via probabilistic segmentation by expectation-maximization. In Computer Vision and Pattern Recognition (CVPR) (2005), vol. 1.
– reference: Darabi S., Shechtman E., Barnes C., Goldman D.B., Sen P.: Image melding: combining inconsistent images using patch-based synthesis. ACM Transactions on Graphics (Proc. SIGGRAPH) 31, 4 (2012), 82.
– reference: Barnes C., Shechtman E., Finkelstein A., Goldman D.: Patchmatch: a randomized correspondence algorithm for structural image editing. ACM Transactions on Graphics (Proc. SIGGRAPH) 28, 3 (2009), 24.
– reference: Diamanti O., Barnes C., Paris S., Shechtman E., Sorkine-Hornung O.: Synthesis of complex image appearance from limited exemplars. ACM Transactions on Graphics 34, 2 (2015).
– reference: Varma M., Zisserman A.: A statistical approach to texture classification from single images. International Journal of Computer Vision 62, 1-2 (2005), 61-81.
– reference: Welsh T., Ashikhmin M., Mueller K.: Transferring color to greyscale images. ACM Transactions on Graphics (Proc. SIGGRAPH) 21, 3 (2002), 277-280.
– reference: Shrivastava A., Malisiewicz T., Gupta A., Efros A.A.: Data-driven visual similarity for cross-domain image matching. ACM Transaction of Graphics(Proc. of ACM SIGGRAPH Asia) 30, 6 (2011).
– reference: An X., Pellacini F.: User-controllable color transfer. Computer Graphics Forum 29, 2 (2010), 263-271.
– reference: Freeman W.T., Jones T.R., Pasztor E.C.: Example-based super-resolution. Computer Graphics and Applications, IEEE 22, 2 (2002), 56-65.
– reference: Shih Y., Paris S., Durand F., Freeman W.T.: Data-driven hallucination of different times of day from a single outdoor photo. ACM Transactions on Graphics (TOG) 32, 6 (2013).
– reference: Kopf J., Kienzle W., Drucker S., Kang S.B.: Quality prediction for image completion. ACM Transactions on Graphics (Proc. SIGGRAPH Asia) 31, 6 (2012).
– reference: Bénard P., Cole F., Kass M., Mordatch I., Hegarty J., Senn M.S., Fleischer K., Pesare D., Breeden K.: Stylizing animation by example. ACM Transactions on Graphics 32, 4 (2013), 119.
– reference: Leung T., Malik J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal Computer Vision 43, 1 (2001).
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Snippet 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...
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...
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StartPage 53
SubjectTerms 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
Title Unifying Color and Texture Transfer for Predictive Appearance Manipulation
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Volume 34
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