This AI Designs Beautiful Forest Landscapes For Games | Game Futurology #41

This is episode #41 of the video series "Game Futurology" covering the paper "Data-driven Authoring of Large-scale Ecosystems" by Konrad Kapp, James Gain, Eric Guerin, Eric Galin and Adrien Peytavie.
PDF: https://perso.liris.cnrs.fr/eric.gali...
Authors' Video:    • Data-driven Authoring of Large-scale ...  

Game Futurology:
This is a video series consisting of short 2-3 minute overview of research papers in the field of AI and Game Development. This series aims to ponder over what the future games might look like based on the latest academic research going on in the field today. Subscribe for more weekly videos!

Abstract:
In computer graphics populating a large-scale natural scene with plants
in a fashion that both reflects the complex interrelationships and diversity
present in real ecosystems and is computationally efficient enough to support iterative authoring remains an open problem. Ecosystem simulations embody many of the botanical influences, such as sunlight, temperature, and moisture, but require hours to complete, while synthesis from statistical distributions tends not to capture fine-scale variety and complexity.
Instead, we leverage real-world data and machine learning to derive
a canopy height model (CHM) for unseen terrain provided by the user. Trees in the canopy layer are then fitted to the resulting CHM through a constrained iterative process that optimizes for a given distribution of species, and, finally, an understorey layer is synthesised using distributions
derived from biome-specific undergrowth simulations. Such a hybrid datadriven approach has the advantage that it incorporates subtle biotic, abiotic, and disturbance factors implicitly encoded in the source data and evidences accepted biological behaviour, such as self-thinning, climatic adaptation, and gap dynamics.


Music Credits: https://www.fesliyanstudios.com/


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