Abstract
This paper proposes a novel framework for improving procedural generation of 3D landscapes using machine learning. We utilized a Deep Convolutional Generative Adversarial Network (DC-GAN) to generate heightmaps. The network was trained on a dataset consisting of Digital Elevation Maps (DEM) of the alps. During map generation, the batch size and learning rate were optimized for the most efficient and satisfying map production. The diversity of the final output was tested against Perlin noise using Mean Square Error [1] and Structure Similarity Index [2]. Perlin noise is especially interesting as it has been used to generate game maps in previous productions [3, 4]. The diversity test showed the generated maps had a significantly greater diversity than the Perlin noise maps. Afterwards the heightmaps was converted to 3D maps in Unity3D. The 3D maps’ perceived realism and videogame usability was pilot tested, showing a promising future for DC-GAN generated 3D landscapes.
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Wulff-Jensen, A., Rant, N.N., Møller, T.N., Billeskov, J.A. (2018). Deep Convolutional Generative Adversarial Network for Procedural 3D Landscape Generation Based on DEM. In: Brooks, A., Brooks, E., Vidakis, N. (eds) Interactivity, Game Creation, Design, Learning, and Innovation. ArtsIT DLI 2017 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 229. Springer, Cham. https://doi.org/10.1007/978-3-319-76908-0_9
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