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Article Dans Une Revue Computer Graphics Forum Année : 2023

Interactive Authoring of Terrain using Diffusion Models

J. Lochner
J. Gain
A. Peytavie
E. Galin

Résumé

Abstract 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 pre‐trained models are available.
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Dates et versions

hal-04324336 , version 1 (05-12-2023)

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J. Lochner, J. Gain, S. Perche, A. Peytavie, E. Galin, et al.. Interactive Authoring of Terrain using Diffusion Models. Computer Graphics Forum, 2023, ⟨10.1111/cgf.14941⟩. ⟨hal-04324336⟩
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