Guehl, PascalAllègre, RemiDischler, Jean-MichelBenes, BedrichGalin, EricDachsbacher, Carsten and Pharr, Matt2020-06-282020-06-2820201467-8659https://doi.org/10.1111/cgf.14061https://diglib.eg.org:443/handle/10.1111/cgf14061We introduce a novel semi-procedural approach that avoids drawbacks of procedural textures and leverages advantages of datadriven texture synthesis. We split synthesis in two parts: 1) structure synthesis, based on a procedural parametric model and 2) color details synthesis, being data-driven. The procedural model consists of a generic Point Process Texture Basis Function (PPTBF), which extends sparse convolution noises by defining rich convolution kernels. They consist of a window function multiplied with a correlated statistical mixture of Gabor functions, both designed to encapsulate a large span of common spatial stochastic structures, including cells, cracks, grains, scratches, spots, stains, and waves. Parameters can be prescribed automatically by supplying binary structure exemplars. As for noise-based Gaussian textures, the PPTBF is used as stand-alone function, avoiding classification tasks that occur when handling multiple procedural assets. Because the PPTBF is based on a single set of parameters it allows for continuous transitions between different visual structures and an easy control over its visual characteristics. Color is consistently synthesized from the exemplar using a multiscale parallel texture synthesis by numbers, constrained by the PPTBF. The generated textures are parametric, infinite and avoid repetition. The data-driven part is automatic and guarantees strong visual resemblance with inputs.Attribution 4.0 International License10.1111/cgf.14061159-171