Realistic and Textured Terrain Generation using GANs
| Autor: | James Alfred Walker, Ryan Spick |
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| Rok vydání: | 2019 |
| Předmět: |
Similarity (geometry)
Computer science business.industry Deep learning 0211 other engineering and technologies Terrain 02 engineering and technology computer.software_genre Set (abstract data type) Computer graphics Software Virtual machine Computer graphics (images) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer 3D computer graphics ComputingMethodologies_COMPUTERGRAPHICS 021101 geological & geomatics engineering |
| Zdroj: | European Conference on Visual Media Production. |
| DOI: | 10.1145/3359998.3369407 |
| Popis: | In computer graphics and virtual environment development, a large portion of time is spent creating assets - one of these being the terrain environment, which usually forms the basis of many large graphical worlds. The texturing of height maps is usually performed as a post-processing step - with software requiring access to the height and gradient of the terrain in order to generate a set of conditions for colouring slopes, flats, mountains etc. With further additions such as biomes specifying which predominant texturing the region should exhibit such as grass, snow, dirt etc. much like the real-world. These methods combined with a height map generation algorithm can create impressive terrain renders which look visually stunning - however can appear somewhat repetitive. Previous work has explored the use of variants of Generative Adversarial Networks for the learning of elevation data through real-world data sets of world height data. In this paper, a method is proposed for learning not only the height map values but also the corresponding satellite image of a specific region. This data is trained through a non-spatially dependant generative adversarial network, which can produce an endless amount of variants of a specific region. The textured outputs are measured using existing similarity metrics and compared to the original region, which yields strong results. Additionally, a visual and statistical comparison of other deep learning image synthesis techniques is performed. The network outputs are also rendered in a 3D graphics engine and visualised in the paper. This method produces powerful outputs when compared directly with the training region, creating a tool that can produce many different variants of the target terrain. This is ideally suited for the use of a developer wanting a large number of specific structures of terrain. |
| Databáze: | OpenAIRE |
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