42-Issue 7
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Browsing 42-Issue 7 by Author "Galin, Eric"
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Item Authoring Terrains with Spatialised Style(The Eurographics Association and John Wiley & Sons Ltd., 2023) Perche, Simon; Peytavie, Adrien; Benes, Bedrich; Galin, Eric; Guérin, Eric; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.Various terrain modelling methods have been proposed for the past decades, providing efficient and often interactive authoring tools. However, they seldom include any notion of style, which is critical for designers in the entertainment industry. We introduce a new generative network method that bridges the gap between automatic terrain synthesis and authoring, providing a versatile set of authoring tools allowing spatialised style. We build upon the StyleGAN2 architecture and extend it with authoring tools. Given an input sketch or existing elevation map, our method generates a terrain with features that can be authored, enhanced, and augmented using interactive brushes and style manipulation tools. The strength of our approach lies in the versatility and interoperability of the different tools. We validate our method quantitatively with drainage calculation against other previous techniques and qualitatively by asking users to follow a prompt or freely create a terrain.Item 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.