Browsing by Author "Gain, James"

Now showing 1 - 4 of 4
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    Accurate Synthesis of Multi-Class Disk Distributions
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Ecormier-Nocca, Pierre; Memari, Pooran; Gain, James; Cani, Marie-Paule; Alliez, Pierre and Pellacini, Fabio
    While analysing and synthesising 2D distributions of points has been applied both to the generation of textures with discrete elements and for populating virtual worlds with 3D objects, the results are often inaccurate since the spatial extent of objects cannot be expressed.We introduce three improvements enabling the synthesis of more general distributions of elements. First, we extend continuous pair correlation function (PCF) algorithms to multi-class distributions using a dependency graph, thereby capturing interrelationships between distinct categories of objects. Second, we introduce a new normalised metric for disks, which makes the method applicable to both point and possibly overlapping disk distributions. The metric is specifically designed to distinguish perceptually salient features, such as disjoint, tangent, overlapping, or nested disks. Finally, we pay particular attention to convergence of the mean PCF as well as the validity of individual PCFs, by taking into consideration the variance of the input. Our results demonstrate that this framework can capture and reproduce real-life distributions of elements representing a variety of complex semi-structured patterns, from the interaction between trees and the understorey in a forest to droplets of water. More generally, it applies to any category of 2D object whose shape is better represented by bounding circles than points.
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    Gradient Terrain Authoring
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Guérin, Eric; Peytavie, Adrien; Masnou, Simon; Digne, Julie; Sauvage, Basile; Gain, James; Galin, Eric; Chaine, Raphaëlle; Kim, Min H.
    Digital terrains are a foundational element in the computer-generated depiction of natural scenes. Given the variety and complexity of real-world landforms, there is a need for authoring solutions that achieve perceptually realistic outcomes without sacrificing artistic control. In this paper, we propose setting aside the elevation domain in favour of modelling in the gradient domain. Such a slope-based representation is height independent and allows a seamless blending of disparate landforms from procedural, simulation, and real-world sources. For output, an elevation model can always be recovered using Poisson reconstruction, which can include Dirichlet conditions to constrain the elevation of points and curves. In terms of authoring our approach has numerous benefits. It provides artists with a complete toolbox, including: cut-and-paste operations that support warping as needed to fit the destination terrain, brushes to modify region characteristics, and sketching to provide point and curve constraints on both elevation and gradient. It is also a unifying representation that enables the inclusion of tools from the spectrum of existing procedural and simulation methods, such as painting localised high-frequency noise or hydraulic erosion, without breaking the formalism. Finally, our constrained reconstruction is GPU optimized and executes in real-time, which promotes productive cycles of iterative authoring.
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    Interactive Authoring of Terrain using Diffusion Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Lochner, Joshua; Gain, James; Perche, Simon; Peytavie, Adrien; Galin, Eric; Guérin, Eric; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    Generating heightfield terrains is a necessary precursor to the depiction of computer-generated natural scenes in a variety of applications. Authoring such terrains is made challenging by the need for interactive feedback, effective user control, and perceptually realistic output encompassing a range of landforms.We address these challenges by developing a terrain-authoring framework underpinned by an adaptation of diffusion models for conditional image synthesis, trained on real-world elevation data. This framework supports automated cleaning of the training set; authoring control through style selection and feature sketches; the ability to import and freely edit pre-existing terrains, and resolution amplification up to the limits of the source data. Our framework improves on previous machine-learning approaches by: expanding landform variety beyond mountainous terrain to encompass cliffs, canyons, and plains; providing a better balance between terseness and specificity in user control, and improving the fidelity of global terrain structure and perceptual realism. This is demonstrated through drainage simulations and a user study testing the perceived realism for different classes of terrain. The full source code, blender add-on, and pretrained models are available.
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    A Review of Digital Terrain Modeling
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Galin, Eric; Guérin, Eric; Peytavie, Adrien; Cordonnier, Guillaume; Cani, Marie-Paule; Benes, Bedrich; Gain, James; Giachetti, Andrea and Rushmeyer, Holly
    Terrains are a crucial component of three-dimensional scenes and are present in many Computer Graphics applications. Terrain modeling methods focus on capturing landforms in all their intricate detail, including eroded valleys arising from the interplay of varied phenomena, dendritic mountain ranges, and complex river networks. Set against this visual complexity is the need for user control over terrain features, without which designers are unable to adequately express their artistic intent. This article provides an overview of current terrain modeling and authoring techniques, organized according to three categories: procedural modeling, physically-based simulation of erosion and land formation processes, and example-based methods driven by scanned terrain data. We compare and contrast these techniques according to several criteria, specifically: the variety of achievable landforms; realism from both a perceptual and geomorphological perspective; issues of scale in terms of terrain extent and sampling precision; the different interaction metaphors and attendant forms of user-control, and computation and memory performance. We conclude with an in-depth discussion of possible research directions and outstanding technical and scientific challenges.