a) Noise based on a continuous target spectrum. (b) Structure-preserving procedural texture deduced from an example. Figure 1: Local random-phase noise can approximate an arbitrary power spectral density. It provides high-speed procedural reproduction of Gaussian patterns defined by a continuous spectrum (a) or by an input example. A broader range of procedural textures by example can be generated by preserving input structures such as skin wrinkles in (b). AbstractLocal random-phase noise is a noise model for procedural texturing. It is defined on a regular spatial grid by local noises, which are sums of cosines with random phase. Our model is versatile thanks to separate sampling in the spatial and spectral domains. Therefore, it encompasses Gabor noise and noise by Fourier series. A stratified spectral sampling allows for a faithful yet compact and efficient reproduction of an arbitrary power spectrum. Noise by example is therefore obtained faster than state-of-the-art techniques. As a second contribution we address texture by example and generate not only Gaussian patterns but also structured features present in the input. This is achieved by fixing the phase on some part of the spectrum. Generated textures are continuous and non-repetitive. Results show unprecedented framerates and a flexible visual result: users can control with one parameter the blending between noise by example and structured texture synthesis.

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In computer graphics, rendering visually detailed scenes is often achieved through texturing. We propose a method for on-the-fly non-periodic infinite texturing of surfaces based on a single image. Pattern repetition is avoided by defining patches within each texture whose content can be changed at runtime. In addition, we consistently manage multi-scale using one input image per represented scale. Undersampling artifacts are avoided by accounting for fine-scale features while colors are transferred between scales. Eventually, we allow for relief-enhanced rendering and provide a tool for intuitive creation of height maps. This is done using an adhoc local descriptor that measures feature self-similarity in order to propagate height values provided by the user for a few selected texels only. Thanks to the patch-based system, manipulated data are compact and our texturing approach is easy to implement on GPU. The multi-scale extension is capable of rendering finely detailed textures in real-time.

International audienceThis paper presents an analytical extension of texture synthesis techniques based on the distribution of elementary texture components. Our approach is similar to the bombing, cellular, macrostructured and lapped textures techniques, but provides the user with more control on both the texture analysis and synthesis phases. Therefore, high quality results can be obtained for a large number of structured or stochastic textures (bricks, marble, lawn, etc.). The analysis consists in decomposing textures into elementary components -- that we call ''texture particles'' -- and for which we analyze their specific spatial arrangements. The synthesis then consists in recomposing similar textures directly on arbitrary surfaces by taking into account the previously computed arrangements, extended to 3D surfaces. Compared to ''pixel-based'' analysis and synthesis methods, which have been recently generalized to arbitrary surfaces, our approach has three major advantages: (1) it is fast, which allows the user to interactively control the synthesis process. This further allows us to propose a large number of tools, granting a high degree of artistic freedom to the user. (2) It avoids the visual deterioration of the texture components by preserving their shapes as well as their spatial arrangements. (3) The texture particles can be not only images, but also 3D geometric elements, which extends significantly the domain of application