“Parallel controllable texture synthesis” by Lefebvre and Hoppe

  • ©Sylvain Lefebvre and Hugues Hoppe




    Parallel controllable texture synthesis



    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.


    1. Arya, S., Mount, D., Netanyahu, N., Silverman, R., and Wu, A. 1998. An optimal algorithm for approximate nearest neighbor searching in fixed dimensions. Journal of the ACM 45(6), 891–923. Google ScholarDigital Library
    2. Ashikhmin, M. 2001. Synthesizing natural textures. Symposium on Interactive 3D Graphics, 217–226. Google ScholarDigital Library
    3. 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. Google ScholarDigital Library
    4. Cohen, M., Shade, J., Hiller, S., and Deussen, O. 2003. Wang tiles for image and texture generation. ACM SIGGRAPH, 287–294. Google ScholarDigital Library
    5. De Bonet, J. 1997. Multiresolution sampling procedure for analysis and synthesis of texture images. ACM SIGGRAPH, 361–368. Google ScholarDigital Library
    6. Efros, A., and Freeman, W. 2001. Image quilting for texture synthesis and transfer. ACM SIGGRAPH, 341–346. Google ScholarDigital Library
    7. Efros, A., and Leung, T. 1999. Texture synthesis by non-parametric sampling. ICCV, 1033–1038. Google ScholarDigital Library
    8. Freeman, W., Pasztor, E., and Carmichael, O. 2000. Learning low-level vision. IJCV 40(1), 25–47. Google ScholarDigital Library
    9. Garber, D. 1981. Computational models for texture analysis and texture synthesis. PhD Dissertation, University of Southern California. Google ScholarDigital Library
    10. Goss, M., and Yuasa, K. 1998. Texture tile visibility determination for dynamic texture loading. Graphics Hardware, 55–60. Google ScholarDigital Library
    11. Hertzmann, A., Jacobs, C., Oliver, N., Curless, B., and Salesin, D. 2001. Image analogies. ACM SIGGRAPH, 327–340. Google ScholarDigital Library
    12. Kraus, M., and Ertl, T. 2002. Adaptive texture maps. Graphics Hardware, 7–15. Google ScholarDigital Library
    13. Kwatra, V., Schödl, A., Essa, I., Turk, G., and Bobick, A. 2003. Graphcut textures: image and video synthesis using graph cuts. ACM SIGGRAPH, 277–286. Google ScholarDigital Library
    14. Lefebvre, S., and Neyret, F. 2003. Pattern based procedural textures. Symposium on Interactive 3D Graphics, 203–212. Google ScholarDigital Library
    15. Liang, L., Liu, C., Xu, Y., Guo, B., and Shum, H.-Y. 2001. Real-time texture synthesis by patch-based sampling. ACM TOG 20(3), 127–150. Google ScholarDigital Library
    16. Liu. Y., Lin, W.-C., and Hays, J. 2004. Near-regular texture analysis and manipulation. ACM SIGGRAPH, 368–376. Google ScholarDigital Library
    17. Liu, Y., Tsin, Y., and Lin, W.-C. 2005. The promise and perils of near-regular texture. IJCV 62(1–2), 149–159. Google ScholarDigital Library
    18. Losasso, F., and Hoppe, H. 2004. Geometry clipmaps: terrain rendering using nested regular grids. ACM SIGGRAPH, 769–776. Google ScholarDigital Library
    19. Perlin, K. 2002. Improving noise. ACM SIGGRAPH, 681–682. Google ScholarDigital Library
    20. Popat, K., and Picard, R. 1993. Novel cluster-based probability model for texture synthesis, classification, and compression. Visual Communications and Image Processing, 756–768.Google Scholar
    21. Praun, E., Finkelstein, A., and Hoppe, H. 2000. Lapped textures. ACM SIGGRAPH, 465–470. Google ScholarDigital Library
    22. Tanner, C., Migdal, C., and Jones, M. 1998. The clipmap: A virtual mipmap. ACM SIGGRAPH, 151–158. Google ScholarDigital Library
    23. Tong, X., Zhang, J., Liu, L., Wang, X., Guo, B., and Shum, H.-Y., 2002. Synthesis of bidirectional texture functions on arbitrary surfaces. ACM SIGGRAPH, 665–672. Google ScholarDigital Library
    24. Tonietto, L., and Walter, M. 2002. Towards local control for image-based texture synthesis. In Proceedings of SIBGRAPI 2002 — XV Brazilian Symposium on Computer Graphics and Image Processing. Google ScholarDigital Library
    25. Wei, L.-Y., and Levoy, M. 2000. Fast texture synthesis using tree-structured vector quantization. ACM SIGGRAPH, 479–488. Google ScholarDigital Library
    26. Wei, L.-Y., and Levoy, M. 2003. Order-independent texture synthesis. http://graphics.stanford.edu/papers/texture-synthesis-sig03/. (Earlier version is Stanford University Computer Science TR-2002-01.)Google Scholar
    27. Wei, L.-Y. 2004. Tile-based texture mapping on graphics hardware. Graphics Hardware, 55–64. Google ScholarDigital Library
    28. Zalesny, A., and Van Gool, L. 2001. A compact model for viewpoint dependent texture synthesis. In SMILE 2000: Workshop on 3D Structure from Images, 124–143. Google ScholarDigital Library
    29. Zalesny, A., Ferrari, V., Caenen, G., and Van Gool, L. 2005. Composite texture synthesis. IJCV 62(1-2), 161–176. Google ScholarDigital Library
    30. Zelinka, S., and Garland, M. 2002. Towards real-time texture synthesis with the jump map. Eurographics Workshop on Rendering. Google ScholarDigital Library
    31. Zhang, J., Zhou, K., Velho, L., Guo, B., and Shum, H.-Y. 2003. Synthesis of progressively-variant textures on arbitrary surfaces. ACM SIGGRAPH, 295–302. Google ScholarDigital Library

ACM Digital Library Publication: