research-article

Multiscale texture synthesis

Authors:

Ravi Ramamoorthi,

Eitan GrinspunAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 27, Issue 3

Pages 1 - 8

https://doi.org/10.1145/1360612.1360650

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.

Supplementary Material

MOV File (a51-han.mov)

Download
30.47 MB

References

[1]

Ashikhmin, M. 2001. Synthesizing natural textures. In SI3D, 217--226.

Digital Library

[2]

Bar-Joseph, Z., El-Yaniv, R., Lischinski, D., and Werman, M. 2001. Texture mixing and texture movie synthesis using statistical learning. IEEE TVCG 7, 2, 120--135.

Digital Library

[3]

DeBonet, J. 1997. Multiresolution sampling procedure for analysis and synthesis of texture images. In SIGGRAPH, 361--368.

Digital Library

[4]

Ebert, D. S., Musgrave, F. K., Peachey, D., Perlin, K., and Worley, S. 2003. Texturing and Modeling: A Procedural Approach. Morgan Kaufmann, San Francisco, CA.

Digital Library

[5]

Efros, A., and Freeman, W. 2001. Image quilting for texture synthesis and transfer. In SIGGRAPH, 341--346.

Digital Library

[6]

Efros, A., and Leung, T. 1999. Texture synthesis by non-parametric sampling. In ICCV, 1033--1038.

Digital Library

[7]

Freeman, W. T., Jones, T. R., and Pasztor, E. C. 2001. Examplebased super-resolution. Tech. Rep. TR-2001-30, MERL.

[8]

Han, C., Sun, B., Ramamoorthi, R., and Grinspun, E. 2007. Frequency domain normal map filtering. In SIGGRAPH, 28.

Digital Library

[9]

Heeger, D. J., and Bergen, J. R. 1995. Pyramid-based texture analysis/synthesis. In SIGGRAPH, 229--238.

Digital Library

[10]

Hertzmann, A., Jacobs, C. E., Oliver, N., Curless, B., and Salesin, D. H. 2001. Image analogies. In SIGGRAPH, 327--340.

Digital Library

[11]

Kopf, J., Fu, C.-W., Cohen-Or, D., Deussen, O., Lischinski, D., and Wong, T.-T. 2007. Solid texture synthesis from 2d exemplars. In SIGGRAPH, 2.

Digital Library

[12]

Kwatra, V., Schodl, A., Essa, I., Turk, G., and Bobick, A. 2003. Graphcut textures: Image and video synthesis using graph cuts. In SIGGRAPH, 277--286.

Digital Library

[13]

Kwatra, V., Essa, I., Bobick, A. F., and Kwatra, N. 2005. Texture optimization for example-based synthesis. In SIGGRAPH, 795--802.

Digital Library

[14]

Lefebvre, S., and Hoppe, H. 2005. Parallel controllable texture synthesis. In SIGGRAPH, 777--786.

Digital Library

[15]

Lefebvre, S., and Hoppe, H. 2006. Appearance-space texture synthesis. In SIGGRAPH, 541--548.

Digital Library

[16]

Liang, L., Liu, C., Xu, Y., Guo, B., and Shum, H. 2001. Real-time texture synthesis by patch-based sampling. Tech. Rep. MSR-TR-2001-40, Microsoft Research.

[17]

Matusik, W., Zwicker, M., and Durand, F. 2005. Texture design using a simplicial complex of morphable textures. In SIGGRAPH, 787--794.

Digital Library

[18]

Perlin, K. 1985. An image synthesizer. In SIGGRAPH, 287--296.

Digital Library

[19]

Popat, K., and Picard, R. 1993. Novel cluster-based probability model for texture synthesis, classification, and compression. In SPIE VCIP, 756--768.

[20]

Portilla, J., and Simoncelli, E. 2000. A parametric texture model based on joint statistics of complex wavelet coefficients. IJCV 40, 1, 49--70.

Digital Library

[21]

Praun, E., Finkelstein, A., and Hoppe, H. 2000. Lapped textures. In SIGGRAPH, 465--470.

Digital Library

[22]

Tong, X., Zhang, J., Liu, L., Wang, X., Guo, B., and Shum, H.-Y. 2002. Synthesis of bidirectional texture functions on arbitrary surfaces. In SIGGRAPH, 665--672.

Digital Library

[23]

Tonietto, L., and Walter, M. 2002. Towards local control for image-based texture synthesis. In SIBGRAPI, 252.

Digital Library

[24]

Wei, L., and Levoy, M. 2000. Fast texture synthesis using treestructured vector quantization. In SIGGRAPH, 355--360.

Digital Library

[25]

Wei, L., and Levoy, M. 2002. Order-independent texture synthesis. Tech. Rep. TR-2002-01, Stanford University CS Dept.

[26]

Wei, L.-Y. 2002. Texture synthesis by fixed neighborhood searching. PhD thesis, Stanford University.

Digital Library

[27]

Wu, Q., and Yu, Y. 2004. Feature matching and deformation for texture synthesis. In SIGGRAPH, 364--367.

Digital Library

[28]

Zalesny, A., Ferrari, V., Caenen, G., and Gool, L. V. 2005. Composite texture synthesis. IJCV 62, 1--2, 161--176.

Digital Library

[29]

Zelinka, S., and Garland, M. 2002. Towards real-time texture synthesis with the jump map. In EGWR, 99--104.

Digital Library

[30]

Zhang, J., Zhou, K., Velho, L., Guo, B., and Shum, H.-Y. 2003. Synthesis of progressively-variant textures on arbitrary surfaces. In SIGGRAPH, 295--302.

Digital Library

Cited By

Wolski KDjeacoumar AJavanmardi ASeidel HTheobalt CCordonnier GMyszkowski KDrettakis GPan XLeimkühler T(2024)Learning Images Across Scales Using Adversarial TrainingACM Transactions on Graphics10.1145/365819043:4(1-13)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3658190
Zhang HYang LLi CWu BWang W(2021)ScaffoldGAN: Synthesis of Scaffold Materials based on Generative Adversarial NetworksComputer-Aided Design10.1016/j.cad.2021.103041138(103041)Online publication date: Sep-2021
https://doi.org/10.1016/j.cad.2021.103041
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
Show More Cited By

Index Terms

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

Recommendations

Inverse texture synthesis

The quality and speed of most texture synthesis algorithms depend on a 2D input sample that is small and contains enough texture variations. However, little research exists on how to acquire such sample. For homogeneous patterns this can be achieved via ...
A Conditional Multiscale Locally Gaussian Texture Synthesis Algorithm

Exemplar-based texture synthesis is defined as the process of generating, from an input texture sample, new texture images that are perceptually equivalent to the input. In the present work, we model texture self-similarity with conditional Gaussian ...
Three-Dimensional Multiscale Texture Synthesis
IITSI '10: Proceedings of the 2010 Third International Symposium on Intelligent Information Technology and Security Informatics

Traditional methods for texture synthesis can only capture a limited band of spatial scales. If we wish to synthesize a texture at a variety of zoom levels, an impractical high input resolution must be required. In this paper, we present a new method ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 27, Issue 3

August 2008

844 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1360612

Issue’s Table of Contents

Copyright © 2008 ACM.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 2008

Published in TOG Volume 27, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

66
Total Citations
View Citations
2,070
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)1

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wolski KDjeacoumar AJavanmardi ASeidel HTheobalt CCordonnier GMyszkowski KDrettakis GPan XLeimkühler T(2024)Learning Images Across Scales Using Adversarial TrainingACM Transactions on Graphics10.1145/365819043:4(1-13)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3658190
Zhang HYang LLi CWu BWang W(2021)ScaffoldGAN: Synthesis of Scaffold Materials based on Generative Adversarial NetworksComputer-Aided Design10.1016/j.cad.2021.103041138(103041)Online publication date: Sep-2021
https://doi.org/10.1016/j.cad.2021.103041
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 Computer Software10.1134/S036176881904007845:4(187-195)Online publication date: 6-Aug-2019
https://doi.org/10.1134/S0361768819040078
Klein TMindek PAutin LGoodsell DOlson AGröller EViola I(2019)Parallel Generation and Visualization of Bacterial Genome StructuresComputer Graphics Forum10.1111/cgf.1381638:7(57-68)Online publication date: 14-Nov-2019
https://doi.org/10.1111/cgf.13816
Sýkora DJamriška OTexler OFišer JLukáč MLu JShechtman E(2019)StyleBlit: Fast Example‐Based Stylization with Local GuidanceComputer Graphics Forum10.1111/cgf.1362138:2(83-91)Online publication date: 7-Jun-2019
https://doi.org/10.1111/cgf.13621
Galerne BLeclaire ARabin J(2018)A Texture Synthesis Model Based on Semi-Discrete Optimal Transport in Patch SpaceSIAM Journal on Imaging Sciences10.1137/18M117578111:4(2456-2493)Online publication date: 25-Oct-2018
https://doi.org/10.1137/18M1175781
Qin ZLi MWu B(2018)Robust Steganography via Patch-Based Texture SynthesisInternet Multimedia Computing and Service10.1007/978-981-10-8530-7_42(429-439)Online publication date: 1-Mar-2018
https://doi.org/10.1007/978-981-10-8530-7_42
Snelgrove XGutierrez DHuang H(2017)High-resolution multi-scale neural texture synthesisSIGGRAPH Asia 2017 Technical Briefs10.1145/3145749.3149449(1-4)Online publication date: 27-Nov-2017
https://dl.acm.org/doi/10.1145/3145749.3149449
Bangay S(2017)Deterministic procedural generation of mesh detail through gradient tilingProceedings of the Australasian Computer Science Week Multiconference10.1145/3014812.3014828(1-10)Online publication date: 30-Jan-2017
https://dl.acm.org/doi/10.1145/3014812.3014828
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents