ODeform: Learning Continuous 4D Motion for Shape Deformation with Neural ODEs
ODeform introduces a novel extension of Neural Ordinary Differential Equations to model continuous 4D dynamics of deformable objects in 3D space. It transforms 3D point clouds and physical conditions into a unified latent space and solves ODEs over time for continuous deformation flows, eliminating discrete time steps while maintaining efficiency. Experiments show improved motion prediction accuracy on unseen physical parameters and successful transfer to real 3D objects. The code and data will be made public.
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[Submitted on 22 Jul 2026]
Title:ODeform: Learning Continuous 4D Motion for Shape Deformation with Neural ODEs
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Abstract:Modeling continuous object deformation is important for many computer vision and robotics tasks, such as manipulation and simulation. Existing approaches rely on learning-based methods or physics simulators to model shape deformations. However, these approaches either use discrete time steps or are too computationally intensive for real-time applications. We present ODeform, a novel extension of Neural Ordinary Differential Equations to continuous 4D dynamics of deformable objects in 3D space. Our method transforms 3D point clouds and physical conditions (like material properties) into a unified latent space. By solving the resulting ordinary differential equations over time, we model deformations as continuous flows within this learned embedding, eliminating the need for discrete time steps while maintaining computational efficiency. We evaluate our approach on unseen physical parameter configurations, showing improved motion prediction accuracy over baseline methods. Our experiments further demonstrate a successful transfer to real 3D captured objects with novel shapes, along with effective interpolation and extrapolation of the learned dynamics. Our code and data will be made publicly available.
Comments: Accepted at IROS 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.20670 [cs.CV]
(or arXiv:2607.20670v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.20670
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yordanka Velikova [view email] [v1] Wed, 22 Jul 2026 19:08:31 UTC (6,526 KB)
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