Wind on Trees: Testing Physical Grounding in Dynamic 4D Gaussian Splatting

Weiying Chen; Edmond Lou

cs.CVSep 15, 2026

Wind on Trees: Testing Physical Grounding in Dynamic 4D Gaussian Splatting

Authors: Weiying Chen, Edmond Lou

Organizations: University of Alberta

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.

Figures & tables

Figure 1: Does a reconstruction recover physics, or replay an animation? Both models are trained on the top row’s wind only. Columns 1–2: branch-tip trajectories tracked through the ground-truth frames, over the 300-frame training window (grey) and continuing into the 50-frame temporal-extrapolation tail (blue). Columns 3–5: a single instant from inside the training window, with the ground-truth canopy outline overlaid on both predictions, so only the wind differs between rows. On the training wind (top), both models sit on the outline: 5.4 px and 2.7 px of canopy-centroid error, and no photometric comparison separates them. Extrapolate the wind to $0.71v_{0}$ (bottom) and the Deformation MLP drifts off the outline (32.6 px) while ours stays on it (0.1 px). Deformation MLP + wind is a Fourier-encoded MLP deformation field over canonical position and time [ 26 ] , additionally conditioned on the preceding 6 s of observed wind subsampled to 48 values, and trained under the same photometric loss as ours.

Figure 2: Overview of Our Method. Our method takes two inputs: a monocular video from a fixed camera, and the wind speed $v(t)$ recorded alongside it. Canonical Gaussians are hard-assigned to $K$ rigid parts by $k$ -means on position (colours are parts), and each part learns $\omega$ , $\zeta$ , a force gain, a swing axis, and a pivot. The wind drives one damped oscillator per part, which differentiable RK4 integrates into a swing angle $\theta(t)$ . Each part is then rigidly rotated by $\theta(t)$ around its pivot, with the rotation applied to Gaussian position and orientation, and rasterized. The wind is a measurement, not supervision: the only training signal is photometric, and nothing supervises $\omega$ or $\zeta$ . Trails are swing paths of tracked canopy points, grey for the observed window and blue for prediction at unseen times, views, and wind speeds. Ground-truth $(\omega,\zeta)$ is held out: it only scores the recovered values, and never enters training.

Figure 3: Qualitative Results across Three Trees and Three Axes of Generalization. Every model is trained on one wind condition, one camera and the first 300 frames. Unseen times are frames beyond that window, unseen views are held-out azimuths, and unseen wind is a condition never trained on ( $0.71v_{0}$ ), evaluated beyond the training window, since at training timestamps a time-indexed field need only replay. Numbers are per-panel PSNR (dB), green where ours is best and orange where a baseline beats it; each panel shows the frame whose ours-minus-baseline gap is closest to that split’s mean.

Figure 4: The collapse is located exactly at the boundary. Per-frame PSNR at the training camera and the training wind, so the only thing that changes at frame 301 is that the timestamps leave the fitted window. On the medium and dense trees, the Deformation MLP tracks ours inside the window, wind-conditioned or not, and then falls away at once past it, below even the undeformed canonical pose, while ours continues. The sparse tree separates nothing here either.

	Sparse (22)		Medium (224)		Dense (562)
	view	time	view	time	view	time
Static pose	24.99	25.16	25.35	24.70	25.08	24.09
Deformation MLP	25.45	25.02	27.88	23.07	26.59	23.19
+ wind	26.39	24.75	27.93	23.53	26.44	23.67
Deformable-3DGS	26.01	24.25	27.66	23.07	26.58	22.65
+ wind	25.49	24.53	27.75	23.56	26.49	23.31

Table 1: Two axes of generalization, all three trees. PSNR (dB). view : held-out azimuths at the training wind and inside the training window, so only the camera is new. time : frames beyond the training window, at the training wind and camera. “Static pose” is the undeformed canonical pose, the reference a deformation model must clear to be doing anything at all. Best per column in bold.

Tree	Model	Median rel. err.	log-log $R^{2}$
Sparse (22 bones)	Untrained null	4.56%	0.981
Sparse (22 bones)	Trained ( $K{=}32$ )	1.80%	0.810
Medium (224 bones)	Untrained null	1.77%	0.996
Medium (224 bones)	Trained ( $K{=}32$ )	1.60%	0.944
Dense (562 bones)	Untrained null	1.56%	0.998
Dense (562 bones)	Trained ( $K{=}32$ )	2.40%	0.843

Table 2: Frequency recovery against an untrained null. The null draws each

\omega_{p}

log-uniformly over the ground-truth range and pushes it through the same solver and the same trajectory matcher as the trained model; entries are the median of 8 draws. Median relative error is taken over matched parts (lower is better);

R^{2}

is that of

\log\hat{\omega}_{p}

against

\log\omega_{p}

. Bold marks the better of each pair on median relative error.

Split	Tree / unseen wind	Static	no wind	+ wind	no wind	+ wind	Ours
			Deformation MLP		Deformable-3DGS
Training timestamps	Sparse (22 bones), $0.84v_{0}$	25.31	25.90	27.04	27.10	27.07	25.84
	Sparse (22 bones), $0.71v_{0}$	25.48	25.82	26.49	26.06	26.13	25.87
	Medium (224 bones), $0.84v_{0}$	27.48	28.95	29.65	29.48	29.70	30.12
	Medium (224 bones), $0.71v_{0}$	28.29	28.07	28.76	28.34	28.69	30.43
	Dense (562 bones), $0.84v_{0}$	26.53	28.27	28.32	27.90	27.79	28.22

Table 3: Zero-shot transfer to unseen wind. PSNR (dB); every model is fit to

v_{0}

only and evaluated on two conditions it never saw. The two blocks differ solely in which frames are scored: training timestamps (top) and frames beyond the training window (bottom). “Static” is the undeformed canonical pose, the reference a deformation model must clear to be doing anything at all. Deformable-3DGS columns are the full-capacity field of [ 26 ] . Best per row in bold.

Ours $-$ (Deformation MLP $+$ wind)	$v_{0}$	$0.84v_{0}$	$0.71v_{0}$
Sparse (22 bones)	$-1.42$	$-1.21$	$-0.61$
Medium (224 bones)	$-0.60$	$+0.46$	$+1.67$
Dense (562 bones)	$-0.03$	$-0.11$	$+0.75$

Table 4: Margin against the wind-conditioned baseline. Each entry is ours minus Deformation MLP

+

wind in PSNR (dB) at training timestamps, on the training condition

v_{0}

and on the two conditions never seen. Positive means ours is ahead.

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Abstract

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