Self Tuning Texture Optimization

The goal of example‐based texture synthesis methods is to generate arbitrarily large textures from limited exemplars in order to fit the exact dimensions and resolution required for a specific modeling task. The challenge is to faithfully capture all of the visual characteristics of the exemplar tex...

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Vydané v:Computer graphics forum Ročník 34; číslo 2; s. 349 - 359
Hlavní autori: Kaspar, Alexandre, Neubert, Boris, Lischinski, Dani, Pauly, Mark, Kopf, Johannes
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.05.2015
Predmet:
Analysis
Automation
Categories and Subject Descriptors (according to ACM CCS)
Channels
I.3.3 [Computer Graphics]: Picture/Image Generation-Line and curve generation
Image processing systems
Optimization
Repetition
Studies
Surface layer
Synthesis
Texture
Visual
ISSN:0167-7055, 1467-8659
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Popis
Shrnutí:The goal of example‐based texture synthesis methods is to generate arbitrarily large textures from limited exemplars in order to fit the exact dimensions and resolution required for a specific modeling task. The challenge is to faithfully capture all of the visual characteristics of the exemplar texture, without introducing obvious repetitions or unnatural looking visual elements. While existing non‐parametric synthesis methods have made remarkable progress towards this goal, most such methods have been demonstrated only on relatively low‐resolution exemplars. Real‐world high resolution textures often contain texture details at multiple scales, which these methods have difficulty reproducing faithfully. In this work, we present a new general‐purpose and fully automatic self‐tuning non‐parametric texture synthesis method that extends Texture Optimization by introducing several key improvements that result in superior synthesis ability. Our method is able to self‐tune its various parameters and weights and focuses on addressing three challenging aspects of texture synthesis: (i) irregular large scale structures are faithfully reproduced through the use of automatically generated and weighted guidance channels; (ii) repetition and smoothing of texture patches is avoided by new spatial uniformity constraints; (iii) a smart initialization strategy is used in order to improve the synthesis of regular and near‐regular textures, without affecting textures that do not exhibit regularities. We demonstrate the versatility and robustness of our completely automatic approach on a variety of challenging high‐resolution texture exemplars.
Bibliografia:istex:5BC8C6B7D9343EA78A9E04760AEABC3AFACC98ED
ArticleID:CGF12565
Supporting Information
ark:/67375/WNG-XH7RHB8N-J
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12565