[Submitted on 15 Sep 2026]
Title:Wind on Trees: Testing Physical Grounding in Dynamic 4D Gaussian Splatting
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Abstract:Monocular reconstruction of wind-driven vegetation is severely underconstrained: motion along the viewing direction is largely unobservable, a moving canopy offers few reliable correspondences, and nearly the entire scene is dynamic, providing little static reference. Directly-learned deformation fields in 4D Gaussian Splatting therefore optimize photometric consistency rather than recover the motion that produced it. We replace that field with a physically parameterized deformation prior: one damped harmonic oscillator per rigid part, driven by the observed wind and integrated by differentiable RK4, supervised photometrically alone. To test whether such a prior is physically grounded rather than merely well fit, we build a controlled synthetic testbed of three procedurally generated trees spanning an order of magnitude in skeleton complexity, whose per-part natural frequency follows from its own geometry and whose damping ratio is a fixed constant, both held out of training. On it, we measure held-out views, temporal extrapolation, zero-shot transfer to unseen wind speeds, and recovery of the physical parameters themselves. The prior costs appearance fidelity on in-distribution views and extrapolates markedly better outside the training window and the training wind, while parameter recovery is far weaker than it first appears: frequency recovery survives an untrained null control on only the sparsest of the three trees, and damping is not recovered at all.
Comments: Workshop Version of ECCV 2026 3DWM Workshop
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.17810 [cs.CV]
(or arXiv:2609.17810v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.17810
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Weiying Chen [view email] [v1] Tue, 15 Sep 2026 20:27:16 UTC (4,561 KB)
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