Learning 3D parametric shape models of objects has gained popularity in vision and graphics and has showed broad utility in 3D reconstruction, generation, understanding, and simulation. While powerful models exist for humans and animals, equally expressive approaches for modeling plants are lacking. In this work, we present Demeter, a data-driven parametric model that encodes key factors of a plant morphology, including topology, shape, articulation, and deformation into a compact learned representation. Unlike previous parametric models, Demeter handles varying shape topology across various species and models three sources of shape variation: articulation, subcomponent shape variation, and non-rigid deformation. To advance crop plant modeling, we collected a large-scale, ground-truthed dataset from a soybean farm as a testbed. Experiments show that Demeter effectively synthesizes shapes, reconstructs structures, and simulates biophysical processes. Code and data is available at https://tianhang-cheng.github.io/Demeter/.
@article{arxiv.2510.16377,
title = {Demeter: A Parametric Model of Crop Plant Morphology from the Real World},
author = {Tianhang Cheng and Albert J. Zhai and Evan Z. Chen and Rui Zhou and Yawen Deng and Zitong Li and Kejie Zhao and Janice Shiu and Qianyu Zhao and Yide Xu and Xinlei Wang and Yuan Shen and Sheng Wang and Lisa Ainsworth and Kaiyu Guan and Shenlong Wang},
journal= {arXiv preprint arXiv:2510.16377},
year = {2025}
}