A generalized framework for procedural generation of three-dimensional static and dynamic plant model geometries
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915688015200256 |
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| author | Bailey, Brian N. |
| author_facet | Bailey, Brian N. |
| contents | This work presents a new framework for procedural generation of dynamic 3D plant model geometries, which has been implemented in the Helios modeling system. Key goals of this work were to develop a model that 1) has a generalized set of parameters that are conserved across species, which are botanically-consistent and readily measurable; 2) significantly reduces the time and effort needed to create photorealistic, dynamically evolving plant models; 3) allows for encoding of the entire plant structure into a character-based representation that can integrated with machine learning models, and 4) includes realistic and computationally efficient collision physics. A model framework that satisfies these specifications is presented in this report. The model was implemented in the Helios C++ and PyHelios Python frameworks, which are open-source libraries that can be used to generate 3D plant geometries based on this model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17966 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A generalized framework for procedural generation of three-dimensional static and dynamic plant model geometries Bailey, Brian N. Other Quantitative Biology This work presents a new framework for procedural generation of dynamic 3D plant model geometries, which has been implemented in the Helios modeling system. Key goals of this work were to develop a model that 1) has a generalized set of parameters that are conserved across species, which are botanically-consistent and readily measurable; 2) significantly reduces the time and effort needed to create photorealistic, dynamically evolving plant models; 3) allows for encoding of the entire plant structure into a character-based representation that can integrated with machine learning models, and 4) includes realistic and computationally efficient collision physics. A model framework that satisfies these specifications is presented in this report. The model was implemented in the Helios C++ and PyHelios Python frameworks, which are open-source libraries that can be used to generate 3D plant geometries based on this model. |
| title | A generalized framework for procedural generation of three-dimensional static and dynamic plant model geometries |
| topic | Other Quantitative Biology |
| url | https://arxiv.org/abs/2512.17966 |