While precise spectral control can be achieved through sparse convolution, corresponding state of the art noise models are typically too expensive for solid noise. We introduce an alternative, wave-based procedural noise model, fast enough to be used in any dimension. We express the noise in the spectral domain and then apply an inverse Fourier transform (FT), requiring the computation of a multidimensional integral. Our contribution is a novel, efficient way to perform this computation, using a sum of precomputed complex-valued hyperplanar wave-functions, oriented in random directions. We show that using suitable wave profiles and combination operators, our model is able to extend to 3D a number of Gaussian and non-Gaussian noises, including Gabor, by-example and Phasor noises, as well as generate novel cellular noises. Our versatile and controllable solid noise model is very compact, a key feature for complex power spectrum and animated noises. We illustrate this through the design of 2D, 3D, and 3D+t materials using color, transparency and style transfer functions.

Abstract By‐example aperiodic tilings are popular texture synthesis techniques that allow a fast, on‐the‐fly generation of unbounded and non‐periodic textures with an appearance matching an arbitrary input sample called the “exemplar”. But by relying on uniform random sampling, these algorithms fail to preserve the autocovariance function, resulting in correlations that do not match the ones in the exemplar. The output can then be perceived as excessively random. In this work, we present a new method which can well preserve the autocovariance function of the exemplar. It consists in fetching contents with an importance sampler taking the explicit autocovariance function as the probability density function (pdf) of the sampler. Our method can be controlled for increasing or decreasing the randomness aspect of the texture. Besides significantly improving synthesis quality for classes of textures characterized by pronounced autocovariance functions, we moreover propose a real‐time tiling and blending scheme that permits the generation of high‐quality textures faster than former algorithms with minimal downsides by reducing the number of texture fetches.

Abstract Volume data these days is usually massive in terms of its topology, multiple fields, or temporal component. With the gap between compute and memory performance widening, the memory subsystem becomes the primary bottleneck for scientific volume visualization. Simple, structured, regular representations are often infeasible because the buses and interconnects involved need to accommodate the data required for interactive rendering. In this state‐of‐the‐art report, we review works focusing on large‐scale volume rendering beyond those typical structured and regular grid representations. We focus primarily on hierarchical and adaptive mesh refinement representations, unstructured meshes, and compressed representations that gained recent popularity. We review works that approach this kind of data using strategies such as out‐of‐core rendering, massive parallelism, and other strategies to cope with the sheer size of the ever‐increasing volume of data produced by today's supercomputers and acquisition devices. We emphasize the data management side of large‐scale volume rendering systems and also include a review of tools that support the various volume data types discussed.

Abstract Stochastic micro‐patterns successfully enhance the realism of virtual scenes. Procedural models using noise combined with transfer functions are extremely efficient. However, most patterns produced today employ 1D transfer functions, which assign color, transparency, or other material attributes, based solely on the single scalar quantity of noise. Multi‐dimensional transfer functions have received widespread attention in other fields, such as scientific volume rendering. But their potential has not yet been well explored for modeling micro‐patterns in the field of procedural texturing. We propose a new procedural model for stochastic patterns, defined as the composition of a bi‐dimensional transfer function (a.k.a. color‐map) with a stochastic vector field. Our model is versatile, as it encompasses several existing procedural noises, including Gaussian noise and phasor noise. It also generates a much larger gamut of patterns, including locally structured patterns which are notoriously difficult to reproduce. We leverage the Gaussian assumption and a tiling and blending algorithm to provide real‐time generation and filtering. A key contribution is a real‐time approximation of the second order statistics over an arbitrary pixel footprint, which enables, in addition, the filtering of procedural normal maps. We exhibit a wide variety of results, including Gaussian patterns, profiled waves, concentric and non‐concentric patterns.

Abstract Stationary Gaussian processes have been used for decades in the context of procedural noises to model and synthesize textures with no spatial organization. In this paper we investigate cyclostationary Gaussian processes, whose statistics are repeated periodically. It enables the modeling of noises having periodic spatial variations, which we call “cyclostationary Gaussian noises”. We adapt to the cyclostationary context several stationary noises along with their synthesis algorithms: spot noise, Gabor noise, local random‐phase noise, high‐performance noise, and phasor noise. We exhibit real‐time synthesis of a variety of visual patterns having periodic spatial variations.

Abstract Physically based rendering of glittering surfaces is a challenging problem in computer graphics. Several methods have proposed off‐line solutions, but none is dedicated to high‐performance graphics. In this work, we propose a novel physically based BRDF for real‐time rendering of glints. Our model can reproduce the appearance of sparkling materials (rocks, rough plastics, glitter fabrics, etc.). Compared to the previous real‐time method [ZK16], which is not physically based, our BRDF uses normalized NDFs and converges to the standard microfacet BRDF [CT82] for a large number of microfacets. Our method procedurally computes NDFs with hundreds of sharp lobes. It relies on a dictionary of 1D marginal distributions: at each location two of them are randomly picked and multiplied (to obtain a NDF), rotated (to increase the variety), and scaled (to control standard deviation/roughness). The dictionary is multiscale, does not depend on roughness, and has a low memory footprint (less than 1 MiB).

Abstract We introduce a novel semi‐procedural approach that avoids drawbacks of procedural textures and leverages advantages of data‐driven 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.

Abstract This paper presents the latest advances and research in Computer Graphics education in a nutshell. It is concerned with topics that were presented at the Education Track of the Eurographics Conference held in Lisbon in 2016. We describe works corresponding to approaches to Computer Graphics education that are unconventional in some way and attempt to tackle unsolved problems and challenges regarding the role of arts in computer graphics education, the role of research‐oriented activities in undergraduate education and the interaction among different areas of Computer Graphics, as well as their application to courses or extra‐curricular activities. We present related works addressing these topics and report experiences, successes and issues in implementing the approaches.

Abstract We propose a bi‐layer representation for textures which is suitable for on‐the‐fly synthesis of unbounded textures from an input exemplar. The goal is to improve the variety of outputs while preserving plausible small‐scale details. The insight is that many natural textures can be decomposed into a series of fine scale Gaussian patterns which have to be faithfully reproduced, and some non‐homogeneous, larger scale structure which can be deformed to add variety. Our key contribution is a novel, bi‐layer representation for such textures. It includes a model for spatially‐varying Gaussian noise, together with a mechanism enabling synchronization with a structure layer. We propose an automatic method to instantiate our bi‐layer model from an input exemplar. At the synthesis stage, the two layers are generated independently, synchronized and added, preserving the consistency of details even when the structure layer has been deformed to increase variety. We show on a variety of complex, real textures, that our method reduces repetition artifacts while preserving a coherent appearance.

Texture synthesis is a well-established area, with many important applications in computer graphics and vision. However, despite their success, synthesis techniques are not used widely in practice because the creation of good exemplars remains challenging and extremely tedious. In this paper, we introduce an unsupervised method for analyzing texture content across multiple scales that automatically extracts good exemplars from natural images. Unlike existing methods, which require extensive manual tuning, our method is fully automatic. This allows the user to focus on using texture palettes derived from their own images, rather than on manual interactions dictated by the needs of an underlying algorithm. Most natural textures exhibit patterns at multiple scales that may vary according to the location (non-stationarity). To handle such textures many synthesis algorithms rely on an analysis of the input and a guidance of the synthesis. Our new analysis is based on a labeling of texture patterns that is both (i) multi-scale and (ii) unsupervised -- that is, patterns are labeled at multiple scales, and the scales and the number of labeled clusters are selected automatically. Our method works in two stages. The first builds a hierarchical extension of superpixels and the second labels the superpixels based on random walk in a graph of similarity between superpixels and a nonnegative matrix factorization. Our label-maps provide descriptors for pixels and regions that benefit state-of-the-art texture synthesis algorithms. We show several applications including guidance of non-stationary synthesis, content selection and texture painting. Our method is designed to treat large inputs and can scale to many megapixels. In addition to traditional exemplar inputs, our method can also handle natural images containing different textured regions.

Local 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.

Abstract Two‐dimensional (2D) parametric colour functions are widely used in Image‐Based Rendering and Image Relighting. They make it possible to express the colour of a point depending on a continuous directional parameter: the viewing or the incident light direction. Producing such functions from acquired data is promising but difficult. Indeed, an intensive acquisition process resulting in dense and uniform sampling is not always possible. Conversely, a simpler acquisition process results in sparse, scattered and noisy data on which parametric functions can hardly be fitted without introducing artefacts. Within this context, we present two contributions. The first one is a robust least‐squares‐based method for fitting 2D parametric colour functions on sparse and scattered data. Our method works for any amount and distribution of acquired data, as well as for any function expressed as a linear combination of basis functions. We tested our fitting for both image‐based rendering (surface light fields) and image relighting using polynomials and spherical harmonics. The second one is a statistical analysis to measure the robustness of any fitting method. This measure assesses a trade‐off between precision of the fitting and stability with respect to input sampling conditions. This analysis along with visual results confirm that our fitting method is robust and reduces reconstruction artefacts for poorly sampled data while preserving the precision for a dense and uniform sampling.

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 ad-hoc 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.

Abstract A procedural pattern generation process, called multi‐scale “assemblage” is introduced. An assemblage is defined as a multi‐scale composition of “multi‐variate” statistical figures, that can be kernel functions for defining noise‐like texture basis functions, or that can be patterns for defining structured procedural textures. This paper presents two main contributions: 1) a new procedural random point distribution function, that, unlike point jittering, allow us to take into account some spatial dependencies among figures and 2) a “multi‐variate” approach that, instead of defining finite sets of constant figures, allows us to generate nearly infinite variations of figures on‐the‐fly. For both, we use a “statistical shape model”, which is a representation of shape variations. Thanks to a direct GPU implementation, assemblage textures can be used to generate new classes of procedural textures for real‐time rendering by preserving all characteristics of usual procedural textures, namely: infinity, definition independency (provided the figures are also definition independent) and extreme compactness.

We introduce procedural texture particles, a new texture model at mid-way between procedural textures and example-based texture synthesis. As for example-based texture synthesis, we use an input example to produce similar looking textures. But instead of creating texture images (pixel arrays), our textures are defined in the form of procedural distributions of interchangeable visual elements called particles. As for classical example-based synthesis, our method guarantees a certain visual resemblance with the example, but obtained textures are compact and defined on the entire infinite 2D plane.

Abstract Noisy volumetric details like clouds, grounds, plaster, bark, roughcast, etc. are frequently encountered in nature and bring an important contribution to the realism of outdoor scenes. We introduce a new interactive approach, easing the creation of procedural representations of “stochastic” volumetric details by using a single example photograph. Instead of attempting to reconstruct an accurate geometric representation from the photograph, we use a stochastic multi‐scale approach that fits parameters of a multi‐layered noise‐based 3D deformation model, using a multi‐resolution filter banks error metric. Once computed, visually similar details can be applied to arbitrary objects with a high degree of visual realism, since lighting and parallax effects are naturally taken into account. Our approach is inspired by image‐based techniques. In practice, the user supplies a photograph of an object covered by noisy details, provides a corresponding coarse approximation of the shape of this object as well as an estimated lighting condition (generally a light source direction). Our system then determines the corresponding noise‐based representation as well as some diffuse, ambient, specular and semi‐transparency reflectance parameters. The resulting details are fully procedural and, as such, have the advantage of extreme compactness, while they can be infinitely extended without repetition in order to cover huge surfaces.

Shading is an important feature for the comprehension of volume datasets, but is difficult to implement accurately. Current techniques based on pre-integrated direct volume rendering approximate the volume rendering integral by ignoring non-linear gradient variations between front and back samples, which might result in cumulated shading errors when gradient variations are important and / or when the illumination function features high frequencies. In this paper, we explore a simple approach for pre-integrated volume rendering with non-linear gradient interpolation between front and back samples. We consider that the gradient smoothly varies along a quadratic curve instead of a segment in-between consecutive samples. This not only allows us to compute more accurate shaded pre-integrated look-up tables, but also allows us to more efficiently process shading amplifying effects, based on gradient filtering. An interesting property is that the pre-integration tables we use remain two-dimensional as for usual pre-integrated classification. We conduct experiments using a full hardware approach with the Blinn-Phong illumination model as well as with a non-photorealistic illumination model.

Classical direct volume rendering techniques accumulate color and opacity contributions using the standard volume rendering equation approximated by alpha blending. However, such standard rendering techniques, often also aiming at visual realism, are not always adequate for efficient data exploration, especially when large opaque areas are present in a data set, since such areas can occlude important features and make them invisible. On the other hand, the use of highly transparent transfer functions allows viewing all the features at once, but often makes these features barely visible. In order to enhance feature visibility, we present in this paper a straightforward rendering technique that consists of modifying the traditional volume rendering equation. Our approach does not require an opacity transfer function, and instead is based on a function quantifying the relative importance of each voxel in the final rendering called relevance function. This function is subsequently used to dynamically adjust the opacity of the contributions per pixel. We conduct experiments with a number of possible relevance functions in order to show the influence of this parameter. As will be shown by our comparative study, our rendering method is much more suitable than standard volume rendering for interactive data exploration at a low extra cost. Thereby, our method avoids feature visibility restrictions without relying on a transfer function and yet maintains a visual similarity with standard volume rendering.

Abstract Hypertexturing can be a powerful way of adding rich geometric details to surfaces at low memory cost by using a procedural three‐dimensional (3D) space distortion. However, this special kind of texturing technique still raises a major problem: the efficient control of the visual result. In this paper, we introduce a framework for interactive hypertexture modelling. This framework is based on two contributions. First, we propose a reformulation of the density modulation function. Our density modulation is based on the notion of shape transfer function. This function, which can be easily edited by users, allows us to control in an intuitive way the visual appearance of the geometric details resulting from the space distortion. Second, we propose to use a hybrid surface and volume‐point‐based representation in order to be able to dynamically hypertexture arbitrary objects at interactive frame rates. The rendering consists in a combined splat‐ and raycasting‐based direct volume rendering technique. The splats are used to model the volumetric object while raycasting allows us to add the details. An experimental study on users shows that our approach improves the design of hypertextures and yet preserves their procedural nature.

In the field of Volume Rendering, the pre-integration of arbitrary transfer functions has certainly led to the most significant and convincing results both quality and performance wise, allowing high quality visualization on standard PC consumer graphics. By showing that the ideal scalar signal along the cast rays is better approximated by a succession of polynomial curves as opposed to linear segments, we propose a new method for pre-integrated volume rendering. This method is based on a second order polynomial interpolation of the scalar values, allowing it to converge more rapidly towards the integration of a volume reconstructed by a trilinear filter. This approach manages to capture the smoothness of the volume's details without the need of further ray resampling, and consequently succeeds in reducing the visual artefacts in comparison to previous techniques. Futhermore, we adapt an existing technique to compute our pre-integration tables using the GPU, thus making our approach suitable for transfer function manipulations.

In this paper we propose a new volumetric implicit surface data structure as an alternative representation to perform mesh processing algorithms. We introduce a new triangulation algorithm suitable for our voxel-based volumetric structure which is a vector field distance transform from an explicit mesh. This new representation is an extension of the implicit scalar field distance function of a mesh. We show our new vector field function is more accurate than the classic scalar field function by comparing both representations with an error metric evaluation. We adapt to this new vector representation mesh integration operation in the reconstruction process of 3D objects from scanned data which was previously performed on the implicit scalar field function. Results show that using this new vector field representation, our mesh integration and mesh triangulation algorithm designs outperform the previous processes based on the implicit scalar field function representation.

Accurate refraction, thanks to raytracing, has always been a popular effect in computer graphics imagery. However, its use has been severely hindered in interactive rendering due to the lack of efficient and realistic techniques geared toward polygon oriented rendering.In this paper, a method to achieve realistic and interactive refractive effects through complex static geometry is proposed. It relies on an offline step where many light paths through the object are pre-evaluated. During rendering, these precomputed paths are used to provide approximations of actual refracted paths through the geometry, enabling further sampling of an environment map. Light paths valuable information, namely final output direction when leaving refractive object, is compressed using frequency domain based spherical harmonics. The matching decompression procedure, entirely offloaded onto graphics hardware, is handled at interactive speed.

This 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.

The representation of ocean waves is not a resolved problem in computer graphics yet. There is still no existing method that allows one to simply describe an agitated surface of any size that is visually sufficiently realistic, without using entirely physical models that are usually very complex. We present a simple method to represent and animate an ocean surface in deep water by considering it as a procedural texture. This texture is defined by a combination of two levels of detail. The first one is a superposition of 2D trochoids whose parameters are determined by ocean wave characteristics infrequency domain. In order to increase the visual complexity of this model and to reduce computation, we incorporate a 3D turbulence function to provide a second level of detail. This turbulence function is also determined by frequency characteristics of ocean waves. Since our synthesized ocean waves spectrum approaches a real ocean waves spectrum, we obtain realistic water waves in the spatial domain. The animation of our model is performed by shifting the phase of the trochoids and by moving into the 3D turbulence function. Since our definition is procedural and continuous, it permits us to obtain any size of water surface with any level of detail as well as a simple, direct, antialiasing method. Our model can be used to generate ocean waves using 2D textures or bump maps as well as 3D textures.

The behavior of light interacting with materials is a crucial factor in achieving a high degree of realism in image synthesis. Local illumination processes, describing the interactions between a point of the surface and a shading ray, are evaluated by bidirectional reflectance distribution functions (BRDFs). Current theoretical BRDFs use surface models restricted to roughness only, sometimes at different scales. We present a more complete surface micro-geometry description, suitable for some common surface defects, including porosity and micro-cracks; both of them are crucial surface features since they strongly influence light reflection properties. These new features are modeled by holes inserted in the surface profile, depending on two parameters: the proportion of surface covered by the defects and the mean geometric characteristic of these defects. In order to preserve the advantages and characteristics of existing BRDFs, a postprocessing method is adopted (we integrate our technique into existing models, instead of defining a completely new one). Beyond providing graphical results closely matching real behaviors, this method moreover opens the way to various important new considerations in computer graphics (for example, changes of appearance due to the degree of humidity).

Analytical approaches, based on digitised 2D texture models, for an automatic solid (3D) texture synthesis have been recently introduced to Computer Graphics. However, these approaches cannot provide satisfactory solutions in the usual case of natural anisotropic textures (wood grain for example). Indeed, solid texture synthesis requires particular care, and sometimes external knowledge to "guess" the internal structure of solid textures because only 2D texture models are used for analysis. By making some basic assumptions about the internal structure of solid textures, we propose a very efficient method based on a hybrid analysis (spectral and histogram) for an automatic synthesis of solid textures. This new method allows us to obtain high precision solid textures (closely resembling initial models) in a large number of cases, including the difficult case of anisotropic textures.

In this paper we describe a method for automatically generating procedural “geometric” textures, using a hybrid (spectral and spatial) analysis of profiles (ID curves). The profile describes a certain height variation for a certain abscissa. We call “geometric” textures a class of textures including “Bump” textures and “hypertextures”. In dealing with this challenge (automatic synthesis), we introduce two new key ideas. The first is to compute efficiently a compact and procedural description of the texture, by using a spectral (Fourier transform based) and spatial (histogram based) analytical approach of profiles. The resulting texture is defined as a sum of elementary random functions. This sum is generated according to the profile. The procedural description allows the direct computation of the texture values at any co‐ordinates in the Euclidean space and the stochastic aspect of the elementary functions also allows for the processing of highly random textures, with no considerable increase of computation time. The second key idea consists of using analysis in the particular and more complex case of geometric textures. For most analytical methods, underlying geometry is never considered. Using profiles as models, the resulting geometric texture is obtained by extending this profile to 2D or 3D space. The resulting texture directly matches the supplied 1D model. Hence, our method promises to be a very useful tool for easily and efficiently “modelling” any kind of geometric textures including rocks, bumps, peaks, fur, cotton and so on.

Abstract Solid (30) texturing is commonly used in computer graphics for producing more realistic images. It is often more attractive than the conventional 20 texture mapping but remains more complex on some points. Its major difficulty concerns the generation of 30 texture in a general and efficient way. The well‐known traditional procedural methods use generally a simplified mathematical model of a natural texture. No reliable way for the choice of the mathematical model parameters, which characterise directly the produced 30 texture, is given. Therefore, 30 texture generation becomes a more or less experimental process with these methods. Our recently published methodfor an automatic 30 texture generation avoids this problem by the use of the spectral analysis of one 2D model texture. The resulting 30 texture is of good quality but one open problem remains: the aspect of the produced texture cannot be fully controlled over the entire 30 space by only one 20 spectral analysis. This may be considered as a serious limitation for some kinds of textures representing important variations in any direction. In this paper we present a new and more powerful analytical approach for an automatic 30 texture generation. Contrarily to our previous method, this new approach is not exclusively based on the spectral analysis of only one 20 model. It uses two or three 2D models corresponding to different slices of a 30 texture block, so, the aspect of the produced 3D texture can be controlled more efficiently over the entire 30 space. In addition, a more efficient 30 texture antialiasing, well adapted to this new method is presented.

Abstract Conventional 2D or 3D texturing methods do not permit an efficient simulation of highly complex structured textures like fire, fur, cotton, etc. More recent techniques, using specific kinds of 3D textures, such as hypertextures or texels based on volume rendering algorithms, are more interesting, for the simulation of such special types of textures. Unfortunately, these techniques remain still restricted because either they need a functional modelling of the object, as it is the case of hypertextures, or they are strictly limited to one specific kind of texture, as it is the case of texels. In this paper we present a new approach for applying a wide range of very different types of highly complex structured textures (fur, fire, water drops, cotton, fume, …) on every kind of objects. This method is particularly based on the geometrical information given by a geometrical model (as the polyhedral or CSG modelling). Like hypertextures or texels, our method uses the volume density rendering, but it isfree of the serious above mentioned restrictions of these methods. In addition, it allows an easy and very intuitive control of the global geometrical shape of generated textures. Its manipulation is simple evenfor a novice user.