NeuraLeaf: Neural Parametric Leaf Models with Shape and Deformation Disentanglement
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912524550537216 |
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| author | Yang, Yang Mao, Dongni Santo, Hiroaki Matsushita, Yasuyuki Okura, Fumio |
| author_facet | Yang, Yang Mao, Dongni Santo, Hiroaki Matsushita, Yasuyuki Okura, Fumio |
| contents | We develop a neural parametric model for 3D leaves for plant modeling and reconstruction that are essential for agriculture and computer graphics. While neural parametric models are actively studied for humans and animals, plant leaves present unique challenges due to their diverse shapes and flexible deformation. To this problem, we introduce a neural parametric model for leaves, NeuraLeaf. Capitalizing on the fact that flattened leaf shapes can be approximated as a 2D plane, NeuraLeaf disentangles the leaves' geometry into their 2D base shapes and 3D deformations. This representation allows learning from rich sources of 2D leaf image datasets for the base shapes, and also has the advantage of simultaneously learning textures aligned with the geometry. To model the 3D deformation, we propose a novel skeleton-free skinning model and create a newly captured 3D leaf dataset called DeformLeaf. We show that NeuraLeaf successfully generates a wide range of leaf shapes with deformation, resulting in accurate model fitting to 3D observations like depth maps and point clouds. Our implementation and dataset are available at https://neuraleaf-yang.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12714 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NeuraLeaf: Neural Parametric Leaf Models with Shape and Deformation Disentanglement Yang, Yang Mao, Dongni Santo, Hiroaki Matsushita, Yasuyuki Okura, Fumio Computer Vision and Pattern Recognition Graphics We develop a neural parametric model for 3D leaves for plant modeling and reconstruction that are essential for agriculture and computer graphics. While neural parametric models are actively studied for humans and animals, plant leaves present unique challenges due to their diverse shapes and flexible deformation. To this problem, we introduce a neural parametric model for leaves, NeuraLeaf. Capitalizing on the fact that flattened leaf shapes can be approximated as a 2D plane, NeuraLeaf disentangles the leaves' geometry into their 2D base shapes and 3D deformations. This representation allows learning from rich sources of 2D leaf image datasets for the base shapes, and also has the advantage of simultaneously learning textures aligned with the geometry. To model the 3D deformation, we propose a novel skeleton-free skinning model and create a newly captured 3D leaf dataset called DeformLeaf. We show that NeuraLeaf successfully generates a wide range of leaf shapes with deformation, resulting in accurate model fitting to 3D observations like depth maps and point clouds. Our implementation and dataset are available at https://neuraleaf-yang.github.io/. |
| title | NeuraLeaf: Neural Parametric Leaf Models with Shape and Deformation Disentanglement |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2507.12714 |