research-article

Multiscale texture synthesis

Authors:

Ravi Ramamoorthi,

Eitan GrinspunAuthors Info & Claims

SIGGRAPH '08: ACM SIGGRAPH 2008 papers

Article No.: 51, Pages 1 - 8

https://doi.org/10.1145/1399504.1360650

Published: 01 August 2008 Publication History

Abstract

Example-based texture synthesis algorithms have gained widespread popularity for their ability to take a single input image and create a perceptually similar non-periodic texture. However, previous methods rely on single input exemplars that can capture only a limited band of spatial scales. For example, synthesizing a continent-like appearance at a variety of zoom levels would require an impractically high input resolution. In this paper, we develop a multiscale texture synthesis algorithm. We propose a novel example-based representation, which we call an exemplar graph, that simply requires a few low-resolution input exemplars at different scales. Moreover, by allowing loops in the graph, we can create infinite zooms and infinitely detailed textures that are impossible with current example-based methods. We also introduce a technique that ameliorates inconsistencies in the user's input, and show that the application of this method yields improved interscale coherence and higher visual quality. We demonstrate optimizations for both CPU and GPU implementations of our method, and use them to produce animations with zooming and panning at multiple scales, as well as static gigapixel-sized images with features spanning many spatial scales.

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Cited By

Li HWang LLiu J(2024)A review of deep learning-based image style transfer researchThe Imaging Science Journal10.1080/13682199.2024.2418216(1-23)Online publication date: 23-Oct-2024
https://doi.org/10.1080/13682199.2024.2418216
Li WChen XLi PSorkine-Hornung OChen B(2023)Example-based Motion Synthesis via Generative Motion MatchingACM Transactions on Graphics10.1145/359239542:4(1-12)Online publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1145/3592395
Yu LZheng Q(2023)AI-Enhanced Digital Creativity Design: Content-Style Alignment for Image StylizationIEEE Access10.1109/ACCESS.2023.334244411(143964-143979)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3342444
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Index Terms

Multiscale texture synthesis
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
    2. Rendering

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cover image ACM Conferences

SIGGRAPH '08: ACM SIGGRAPH 2008 papers

August 2008

887 pages

ISBN:9781450301121

DOI:10.1145/1399504

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 August 2008

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SIGGRAPH '08: Special Interest Group on Computer Graphics and Interactive Techniques Conference

August 11 - 15, 2008

California, Los Angeles

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SIGGRAPH '08 Paper Acceptance Rate 90 of 518 submissions, 17%;

Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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Li HWang LLiu J(2024)A review of deep learning-based image style transfer researchThe Imaging Science Journal10.1080/13682199.2024.2418216(1-23)Online publication date: 23-Oct-2024
https://doi.org/10.1080/13682199.2024.2418216
Li WChen XLi PSorkine-Hornung OChen B(2023)Example-based Motion Synthesis via Generative Motion MatchingACM Transactions on Graphics10.1145/359239542:4(1-12)Online publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1145/3592395
Yu LZheng Q(2023)AI-Enhanced Digital Creativity Design: Content-Style Alignment for Image StylizationIEEE Access10.1109/ACCESS.2023.334244411(143964-143979)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3342444
Wu RZheng C(2022)Learning to Generate 3D Shapes from a Single ExampleACM Transactions on Graphics10.1145/3550454.355548041:6(1-19)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1145/3550454.3555480
Scott JDodgson N(2022)Evaluating Realism in Example-based Terrain SynthesisACM Transactions on Applied Perception10.1145/353152619:3(1-18)Online publication date: 2-Sep-2022
https://dl.acm.org/doi/10.1145/3531526
Rang XMa CZheng QZhao G(2022)A Generative Adversarial Network for Multistyle Unsupervised Image Transfer Based on Dynamic Position NormalizationIEEE Access10.1109/ACCESS.2022.320501310(96284-96295)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3205013
Scott JDodgson N(2021)Example-based terrain synthesis with pit removalComputers and Graphics10.1016/j.cag.2021.06.01299:C(43-53)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1016/j.cag.2021.06.012
Sethapakdi TMcCann JBrewster SFitzpatrick GCox AKostakos V(2019)Painting with CATSProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300287(1-9)Online publication date: 2-May-2019
https://dl.acm.org/doi/10.1145/3290605.3300287
Tahmasebi P(2018)Nanoscale and multiresolution models for shale samplesFuel10.1016/j.fuel.2017.12.107217(218-225)Online publication date: Apr-2018
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Argudo OAndujar CChica AGuérin EDigne JPeytavie AGalin E(2017)Coherent multi-layer landscape synthesisThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-017-1393-633:6-8(1005-1015)Online publication date: 1-Jun-2017
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