Exploring Fungal Morphology Simulation and Dynamic Light Containment from a Graphics Generation Perspective

• URL: http://arxiv.org/abs/2409.05171v1
• Date: Sun, 8 Sep 2024 17:50:24 GMT
• Title: Exploring Fungal Morphology Simulation and Dynamic Light Containment from a Graphics Generation Perspective
• Authors: Kexin Wang, Ivy He, Jinke Li, Ali Asadipour, Yitong Sun,
• Abstract summary: This study equates fungal morphology simulation to a two-dimensional graphic time-series generation problem. We propose a zero-coding, neural network-driven cellular automaton. Fungal spread patterns are learned through an image segmentation model and a time-series prediction model, which then supervise the training of neural network cells.
• Score: 7.750079771971273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fungal simulation and control are considered crucial techniques in Bio-Art creation. However, coding algorithms for reliable fungal simulations have posed significant challenges for artists. This study equates fungal morphology simulation to a two-dimensional graphic time-series generation problem. We propose a zero-coding, neural network-driven cellular automaton. Fungal spread patterns are learned through an image segmentation model and a time-series prediction model, which then supervise the training of neural network cells, enabling them to replicate real-world spreading behaviors. We further implemented dynamic containment of fungal boundaries with lasers. Synchronized with the automaton, the fungus successfully spreads into pre-designed complex shapes in reality.

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