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arxiv: 2605.10567 · v1 · submitted 2026-05-11 · 💻 cs.CV

VeloGauss: Learning Physically Consistent Gaussian Velocity Fields from Videos

Nengbo Lu , Bin Zhao This is my paper

Pith reviewed 2026-05-12 04:02 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D Gaussian splattingvelocity fieldsdynamic scenesphysical consistencynovel view synthesisfuture predictionparticle dynamicsmulti-view video
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The pith

VeloGauss learns velocity fields for each Gaussian particle to model physically consistent 3D dynamic scenes from videos without priors.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper tries to establish that physical information in dynamic 3D scenes can be learned jointly with geometry and appearance directly from multi-view videos. It does so by assigning a velocity field to every Gaussian particle through a Physics Code and Particle Dynamics System, then enforcing Global Physical Constraints across the scene. A reader would care if this works because it could lead to more realistic reconstructions and predictions of how objects move and interact in complex scenes, including mixtures of rigid and deformable bodies. The approach avoids the limitations of soft physical losses or integrated simulations by building in mechanisms for consistency.

Core claim

We propose VeloGauss, designed to learn the physical properties of complex dynamic 3D scenes without physical priors. Our method learns the velocity field for each Gaussian particle by introducing a Physics Code and a Particle Dynamics System, and ultimately incorporates Global Physical Constraints to ensure the physical consistency of the scene. Extensive experiments on four public datasets demonstrate that our method achieves state-of-the-art performance in both Novel View Interpolation and Future Frame Extrapolation tasks.

What carries the argument

Per-Gaussian velocity fields learned via a Physics Code and Particle Dynamics System, regularized by Global Physical Constraints.

If this is right

  • Outperforms prior methods in novel view interpolation for dynamic scenes.
  • Improves future frame extrapolation by maintaining physical consistency.
  • Allows correct modeling of interactions between rigid and non-rigid particles.
  • Jointly captures geometry, appearance, and physical information from videos alone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar constraint mechanisms could be applied to other neural scene representations for physics-aware modeling.
  • Success here might reduce the need for separate physics engines in video-based 3D reconstruction pipelines.
  • If the velocity fields generalize, the method could support applications in robotics for predicting object motions from visual input.
  • Testing the constraints on scenes with known physical violations would help confirm their effectiveness.

Load-bearing premise

The Physics Code, Particle Dynamics System, and Global Physical Constraints are enough to capture the correct interaction mechanisms between rigid and non-rigid particles even without any physical priors.

What would settle it

Observing unphysical particle behaviors such as objects passing through each other or incorrect velocity transfers in extrapolated frames from a test video would show the constraints do not ensure consistency.

Figures

Figures reproduced from arXiv: 2605.10567 by Bin Zhao, Nengbo Lu.

Figure 1
Figure 1. Figure 1: Our approach achieves superior efficiency and signifi [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Taking multi-view RGB video streams as input, our method first employs a physics encoder to identify per-particle latent [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Network framework for constructing the Gaussian [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison of rendering results against [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
read the original abstract

In this paper, we aim to jointly model the geometry, appearance, and physical information of 3D scenes solely from dynamic multi-view videos, without relying on any physical priors. Existing works typically employ physical losses merely as soft constraints or integrate physical simulations into neural networks; however, these approaches often fail to effectively learn complex motion physics. Although modeling velocity fields holds the potential to capture authentic physical information, due to the lack of appropriate physical constraints, current methods are unable to correctly learn the interaction mechanisms between rigid and non-rigid particles. To address this, we propose VeloGauss, designed to learn the physical properties of complex dynamic 3D scenes without physical priors. Our method learns the velocity field for each Gaussian particle by introducing a Physics Code and a Particle Dynamics System, and ultimately incorporates Global Physical Constraints to ensure the physical consistency of the scene. Extensive experiments on four public datasets demonstrate that our method outperforms achieves state-of-the-art performance in both Novel View Interpolation and Future Frame Extrapolation tasks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes VeloGauss for jointly modeling geometry, appearance, and physical information of dynamic 3D scenes from multi-view videos without external physical priors. It learns per-Gaussian velocity fields via a Physics Code and Particle Dynamics System, then applies Global Physical Constraints to enforce consistency in rigid/non-rigid particle interactions. Experiments on four public datasets report state-of-the-art results on novel-view interpolation and future-frame extrapolation.

Significance. If the velocity-field formulation and constraints demonstrably produce physically consistent motion without circularity or hidden priors, the work would advance physics-informed dynamic Gaussian splatting. It targets a clear gap in prior methods that rely on soft losses or external simulators, with potential value for motion extrapolation in mixed rigid/deformable scenes.

major comments (2)
  1. [Methods] Methods section (Particle Dynamics System and Global Physical Constraints): the manuscript must supply the explicit update equations and loss terms. Without them it is impossible to verify that the constraints are independent of the learned velocities rather than being satisfied by construction, which directly bears on the central claim of learning physical consistency without priors.
  2. [Experiments] Experiments section (ablation studies): no quantitative ablation isolates the contribution of the Physics Code, Particle Dynamics System, and Global Physical Constraints. The SOTA claim on future-frame extrapolation therefore cannot be attributed to the physical-consistency mechanism rather than to other modeling choices.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'outperforms achieves state-of-the-art' is grammatically incorrect and should be corrected.
  2. [Methods] Notation: the distinction between 'Physics Code' (per-particle?) and 'Global Physical Constraints' (scene-level?) should be clarified with consistent symbols or a diagram.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to incorporate the requested clarifications and additional experiments.

read point-by-point responses

  1. Referee: [Methods] Methods section (Particle Dynamics System and Global Physical Constraints): the manuscript must supply the explicit update equations and loss terms. Without them it is impossible to verify that the constraints are independent of the learned velocities rather than being satisfied by construction, which directly bears on the central claim of learning physical consistency without priors.

    Authors: We agree that explicit formulations are required to substantiate the independence of the constraints. In the revised manuscript we will add the complete update equations for the Particle Dynamics System (including how the Physics Code parameterizes per-particle velocity increments and how positions are integrated over time) together with the precise loss terms for the Global Physical Constraints. These terms are derived from first-principles physical invariants (momentum conservation for rigid clusters and strain-energy bounds for non-rigid deformation) and are applied as additive regularizers; they are not automatically satisfied by the velocity parameterization itself. The added equations will allow direct verification that physical consistency is enforced through optimization rather than by construction. revision: yes

  2. Referee: [Experiments] Experiments section (ablation studies): no quantitative ablation isolates the contribution of the Physics Code, Particle Dynamics System, and Global Physical Constraints. The SOTA claim on future-frame extrapolation therefore cannot be attributed to the physical-consistency mechanism rather than to other modeling choices.

    Authors: We acknowledge the need for component-wise isolation. The revised Experiments section will include quantitative ablations that successively disable (1) the Physics Code (replaced by direct MLP velocity regression), (2) the Particle Dynamics System (fixed velocities), and (3) the Global Physical Constraints (retaining only local photometric and smoothness losses). Performance deltas on future-frame extrapolation across all four datasets will be reported, thereby attributing the observed gains specifically to the physical-consistency components. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The abstract introduces a Physics Code, Particle Dynamics System, and Global Physical Constraints as novel components to learn velocity fields from videos without priors. No equations, self-citations, fitted parameters renamed as predictions, or load-bearing derivations are visible in the provided text. The method is presented as adding independent mechanisms for physical consistency, with no quoted reduction showing that any claimed prediction or constraint is equivalent to its inputs by construction. The derivation chain therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 3 invented entities

The method rests on the assumption that Gaussian particles can carry both appearance and velocity, that a learned code and dynamics system can replace explicit priors, and that global constraints will enforce consistency; these are introduced without independent verification in the abstract.

axioms (2)
  • domain assumption Gaussian particles can jointly represent geometry, appearance, and physical velocity in dynamic scenes
    Core modeling choice stated in the abstract.
  • ad hoc to paper Physical consistency of rigid and non-rigid interactions can be achieved without any external physical priors
    Explicitly claimed as the goal of the proposed components.
invented entities (3)
  • Physics Code no independent evidence
    purpose: Encodes physical properties for each Gaussian particle to support velocity learning
    New component introduced to address the lack of physical constraints in prior Gaussian methods.
  • Particle Dynamics System no independent evidence
    purpose: Models interactions between rigid and non-rigid particles
    New system added to capture complex motion physics.
  • Global Physical Constraints no independent evidence
    purpose: Enforces overall physical consistency of the learned velocity fields
    Final component incorporated to guarantee scene-level consistency.

pith-pipeline@v0.9.0 · 5469 in / 1635 out tokens · 59796 ms · 2026-05-12T04:02:42.312081+00:00 · methodology

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Reference graph

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