Texture Stationarization: Turning Photos into Tileable Textures


Moritz, J., James, S., Fincham Haines, T., Ritschel, T. and Weyrich, T., 2017. Texture Stationarization: Turning Photos into Tileable Textures. In: Eurographics 2017: Annual Conference of the European Association for Computer Graphics, 2017-04-24 - 2017-04-28.

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    Texture synthesis has grown into a mature field in computer graphics, allowing the synthesis of naturalistic textures and images from photographic exemplars. Surprisingly little work, however, has been dedicated to synthesizing tileable textures, that is, textures that when laid out in a regular grid of tiles form a homogeneous appearance suitable for use in memory-sensitive real-time graphics applications. One of the key challenges in doing so is that most natural input exemplars exhibit uneven spatial variations that, when tiled, show as repetitive patterns. We propose an approach to synthesize tileable textures while enforcing stationarity properties that effectively mask repetitions while maintaining the unique characteristics of the exemplar. We explore a number of alternative measures for texture stationarity and show how each measure can be integrated into a standard texture synthesis method (PatchMatch) to enforce stationarity at user-controlled scales. We demonstrate the efficacy of our approach using a database of 118 exemplar images, both from publicly available sources as well as new ones captured under uncontrolled conditions, and we quantitatively analyze alternative stationarity measures for their robustness across many test runs using different random seeds. In conclusion, we suggest a novel synthesis approach that employs local histogram matching to reliably turn input photographs of natural surfaces into tiles well suited for artifact-free tiling.


    Item Type Conference or Workshop Items (Paper)
    CreatorsMoritz, J., James, S., Fincham Haines, T., Ritschel, T. and Weyrich, T.
    DepartmentsFaculty of Science > Computer Science
    Research CentresEPSRC Centre for Doctoral Training in Statistical Mathematics (SAMBa)
    ID Code56748


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