用神经网络让真菌按设计形状生长，无需写代码。
Exploring Fungal Morphology Simulation and Dynamic Light Containment from a Graphics Generation Perspective
- 用图像分割和时序预测模型训练神经元细胞，模拟真菌生长
- 实现真菌在激光控制下精准长成预设复杂形状
- 适合生物艺术创作与交互式生态模拟研究者
真菌模拟与控制是生物艺术创作中的关键技术。然而，编写可靠的真菌模拟算法对艺术家而言仍具挑战。本研究将真菌形态模拟视为二维图形时间序列生成问题，提出一种零编码、基于神经网络的元胞自动机。通过图像分割模型与时序预测模型学习真菌扩散模式，并以此监督神经网络细胞训练，使其能复现真实世界的扩散行为。进一步实现了激光同步的动态边界控制，使真菌在现实中成功扩展为预设的复杂形状。
原文摘要 · Abstract (English)
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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