# NeuraLeaf: Neural Parametric Leaf Models with Shape and Deformation Disentanglement

arXiv: https://arxiv.org/abs/2507.12714 · HTML: https://mlsanity.com/paper/2507.12714/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2025.

## Built on by (most cited first, 5 of 5)

- [GaussianPlant: Structure-aligned Gaussian Splatting for 3D Reconstruction of Plants](https://mlsanity.com/paper/2512.14087/) (2025)
- [FloraForge: LLM-Assisted Procedural Generation of Editable and Analysis-Ready 3D Plant Geometric Models For Agricultural Applications](https://mlsanity.com/paper/2512.11925/) (2025)
- [GrowFields: Compositional 4D Neural Fields for Topology-Changing Plant Growth](https://mlsanity.com/paper/2607.03330/) (2026)
- [Structure-aware Riemannian Growth Fields for 4D Plant Modeling](https://mlsanity.com/paper/2608.13007/) (2026)
- [A Vision-Language Model (VLM)-based Pipeline for End-to-End Procedural Modeling of Field-Grown Maize from Point Clouds](https://mlsanity.com/paper/2610.03468/) (2026)

## Abstract (arXiv)

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/.