article

Parallel controllable texture synthesis

Authors:

Sylvain Lefebvre,

Hugues HoppeAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 24, Issue 3

Pages 777 - 786

https://doi.org/10.1145/1073204.1073261

Published: 01 July 2005 Publication History

Abstract

We present a texture synthesis scheme based on neighborhood matching, with contributions in two areas: parallelism and control. Our scheme defines an infinite, deterministic, aperiodic texture, from which windows can be computed in real-time on a GPU. We attain high-quality synthesis using a new analysis structure called the Gaussian stack, together with a coordinate upsampling step and a subpass correction approach. Texture variation is achieved by multiresolution jittering of exemplar coordinates. Combined with the local support of parallel synthesis, the jitter enables intuitive user controls including multiscale randomness, spatial modulation over both exemplar and output, feature drag-and-drop, and periodicity constraints. We also introduce synthesis magnification, a fast method for amplifying coarse synthesis results to higher resolution.

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Cited By

Zirek S(2023)Synthesising 3d Solid Models of Natural Heterogeneous Materials from Single Sample Image, Using Encoding Deep Convolutional Generative Adversarial NetworksSSRN Electronic Journal10.2139/ssrn.4352254Online publication date: 2023
https://doi.org/10.2139/ssrn.4352254
Barnes CShechtman EFinkelstein AGoldman D(2023)PatchMatch: A Randomized Correspondence Algorithm for Structural Image EditingSeminal Graphics Papers: Pushing the Boundaries, Volume 210.1145/3596711.3596777(619-629)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3596711.3596777
Zirek S(2023)Synthesising 3D solid models of natural heterogeneous materials from single sample image, using encoding deep convolutional generative adversarial networksSystems and Soft Computing10.1016/j.sasc.2023.2000515(200051)Online publication date: Dec-2023
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Index Terms

Parallel controllable texture synthesis

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 24, Issue 3

July 2005

826 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1073204

Issue’s Table of Contents

Copyright © 2005 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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Publication History

Published: 01 July 2005

Published in TOG Volume 24, Issue 3

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Cited By

Zirek S(2023)Synthesising 3d Solid Models of Natural Heterogeneous Materials from Single Sample Image, Using Encoding Deep Convolutional Generative Adversarial NetworksSSRN Electronic Journal10.2139/ssrn.4352254Online publication date: 2023
https://doi.org/10.2139/ssrn.4352254
Barnes CShechtman EFinkelstein AGoldman D(2023)PatchMatch: A Randomized Correspondence Algorithm for Structural Image EditingSeminal Graphics Papers: Pushing the Boundaries, Volume 210.1145/3596711.3596777(619-629)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3596711.3596777
Zirek S(2023)Synthesising 3D solid models of natural heterogeneous materials from single sample image, using encoding deep convolutional generative adversarial networksSystems and Soft Computing10.1016/j.sasc.2023.2000515(200051)Online publication date: Dec-2023
https://doi.org/10.1016/j.sasc.2023.200051
Nečas DKlapetek P(2021)Synthetic Data in Quantitative Scanning Probe MicroscopyNanomaterials10.3390/nano1107174611:7(1746)Online publication date: 2-Jul-2021
https://doi.org/10.3390/nano11071746
Pinto FRomanoni AMatteucci MTorr P(2021)SECI-GAN: Semantic and Edge Completion for dynamic objects removal2020 25th International Conference on Pattern Recognition (ICPR)10.1109/ICPR48806.2021.9413320(10441-10448)Online publication date: 10-Jan-2021
https://doi.org/10.1109/ICPR48806.2021.9413320
Bale AKumar SKiran Mohan MVinay N(2021)A Study of Improved Methods on Image InpaintingTrends and Advancements of Image Processing and Its Applications10.1007/978-3-030-75945-2_15(281-296)Online publication date: 14-Nov-2021
https://doi.org/10.1007/978-3-030-75945-2_15
Guehl PAllègre RDischler JBenes BGalin E(2020)Semi‐Procedural Textures Using Point Process Texture Basis FunctionsComputer Graphics Forum10.1111/cgf.1406139:4(159-171)Online publication date: 20-Jul-2020
https://doi.org/10.1111/cgf.14061
Guingo GLarue FSauvage BLutz NDischler JCani M(2020)Content-aware texture deformation with dynamic controlComputers & Graphics10.1016/j.cag.2020.07.00691(95-107)Online publication date: Oct-2020
https://doi.org/10.1016/j.cag.2020.07.006
Frühstück AAlhashim IWonka P(2019)TileGANACM Transactions on Graphics10.1145/3306346.332299338:4(1-11)Online publication date: 12-Jul-2019
https://dl.acm.org/doi/10.1145/3306346.3322993
Sanzharov VFrolov V(2019)Level of Detail for Precomputed Procedural TexturesProgramming and Computing Software10.1134/S036176881904007845:4(187-195)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1134/S0361768819040078
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