We describe a fully automatic method of synthesizing isotropic textures on subdivision surfaces from sample images. We restrict ourselves to isotropic textures because only isotropic textures can be automatically generated on an arbitrary surface in the absence of a parametrization. Unlike previous approaches, texture synthesis is accomplished in a coarse-to-fine fashion by constructing both Gaussian and Laplacian pyramid representations of the synthetic texture. The inverse Laplacian pyramid transform is used to generate first approximations to the texture at each level of the associated Gaussian pyramid. These approximations are refined using a modified nearest neighbor search process which preserves the first-order statistics of the sample texture. This search process can be considered to be a sampling procedure for an implicitly defined Markov random field. The resulting texture is generated directly on the subdivision surface. Within the domain of isotropic textures, the proposed method offers improvements in faithful reproduction of a sample’s appearance over a wide range of scales
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