Summary of Procedural Terrain Generation with Style Transfer, by Fabio Merizzi
Procedural terrain generation with style transfer
by Fabio Merizzi
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel technique for generating terrain maps by combining procedural generation and Neural Style Transfer. The approach is considered a viable alternative to existing generative models, offering greater versatility, lower hardware requirements, and better integration in the creative process of designers and developers. The method involves generating procedural noise maps using Gaussian noise or Perlin algorithm, followed by an enhanced Neural Style transfer technique that draws style from real-world height maps. This fusion enables the creation of diverse terrains closely aligned with real-world landscapes, with low computational cost and customization capabilities. Numerical evaluations validate the model’s ability to accurately replicate terrain morphology, surpassing traditional procedural methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes it possible to create realistic-looking terrain maps using a new technique that combines two different ways of making maps: one way is by using math formulas (procedural generation), and the other way is by copying styles from real-world height maps (Neural Style Transfer). This combination allows for more variety in the terrains created, with less need for powerful computers. The researchers use different types of noise to create the terrain maps, and then copy the style of real-world maps to make them look more realistic. This new technique can be used by designers and developers to create customized maps that are not only diverse but also similar to real-world landscapes. |
Keywords
» Artificial intelligence » Style transfer
