Exploring Fungal Morphology Simulation and Dynamic Light Containment from a Graphics Generation Perspective
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913493919203328 |
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| author | Wang, Kexin He, Ivy Li, Jinke Asadipour, Ali Sun, Yitong |
| author_facet | Wang, Kexin He, Ivy Li, Jinke Asadipour, Ali Sun, Yitong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05171 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Fungal Morphology Simulation and Dynamic Light Containment from a Graphics Generation Perspective Wang, Kexin He, Ivy Li, Jinke Asadipour, Ali Sun, Yitong Graphics Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning 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. |
| title | Exploring Fungal Morphology Simulation and Dynamic Light Containment from a Graphics Generation Perspective |
| topic | Graphics Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2409.05171 |