Interactive Authoring of Terrain using Diffusion Models

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Date
2023
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Publisher
The Eurographics Association and John Wiley & Sons Ltd.
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 pretrained models are available.
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@article{
10.1111:cgf.14941
, journal = {Computer Graphics Forum}, title = {{
Interactive Authoring of Terrain using Diffusion Models
}}, author = {
Lochner, Joshua
and
Gain, James
and
Perche, Simon
and
Peytavie, Adrien
and
Galin, Eric
and
Guérin, Eric
}, year = {
2023
}, publisher = {
The Eurographics Association and John Wiley & Sons Ltd.
}, ISSN = {
1467-8659
}, DOI = {
10.1111/cgf.14941
} }
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