Exploring Fungal Morphology Simulation and Dynamic Light Containment from a Graphics Generation Perspective
This work addresses challenges in fungal simulation for artists, offering a novel method for Bio-Art creation.
The study tackled the problem of simulating fungal morphology for Bio-Art by framing it as a 2D graphic time-series generation, resulting in a zero-coding neural network-driven cellular automaton that replicates real-world spreading behaviors and enables dynamic containment with lasers to achieve pre-designed complex shapes.
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.