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

arXiv: https://arxiv.org/abs/2409.05171 · HTML: https://mlsanity.com/paper/2409.05171/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 8 Sep 2024.

## Builds on (most cited first, 1 of 1)

- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)

## Built on by (most cited first, 1 of 1)

- [Participatory Evolution of Artificial Life Systems via Semantic Feedback](https://mlsanity.com/paper/2507.03839/) (2025)

## Abstract (arXiv)

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.