Procedural Texture Generation We often generate textures procedurally synthesis from example textures randomized material models e g marble wood granite reaction diffusion chemical reaction any other function we can imagine Closely related to procedural geometry synthesis and a lot of the techniques crossover see book by Ebert et al Texturing Modeling A Procedural Approach Synthesizing Textures How do we efficiently generate textures a common approach is to photograph something real for example photograph some brick apply to our walls this is nice but it s limited all walls have identical bricks Solution 1 Take lots of different photos works but requires lots of storage Solution 2 Synthesize new brick textures start with an example of a given texture synthesize new textures which have similar overall look but whose local features are distinct this is by no means a solved problem Randomized Models Randomized procedural texture models are quite popular start with some regular pattern add in a bunch of noise functions at different frequencies Perlin s noise function is a particularly popular choice he won an Academy Award for developing it used for both 2 D and solid textures hypertexture Cool demos http graphics lcs mit edu legakis MarbleApplet marbleapplet html http mrl nyu edu perlin demox Planet html Reaction Diffusion Methods Imagine that we have some chemicals in the plane they diffuse across space and they react with each other according to differential equations we make up This describes realistic animal patterns appears this may actually account for real stripe spot formation Generating Textures on Arbitrary Surfaces Using Reaction Diffusion Greg Turk SIGGRAPH 91 Demos of reaction diffusion http members aol com ht a jiweichsel wr3 html http www ccsf caltech edu ismap image html Synthesis from Images Generate large amounts of texture from small input samples by semi randomly permuting the input pixels synthesized texture input sample Fast Texture Synthesis using Tree structured Vector Quantization Wei Levoy SIGGRAPH 2000 Image Synthesis Assumptions Different neighborhoods of texture are roughly similar Neighborhood similarity measured by pairwise pixel difference Difference pi q i i 2 One Basic Algorithm input sample Wei Levoy SIGGRAPH 2000 Initially fill with random noise Scan over pixels p in scanline order 1 Collect neighborhood of p 2 Find all similar matches in input 3 Copy pixel from one of them raster scan order Some Sample Results
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