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arxiv: 2605.23845 · v1 · pith:5VZNKTZPnew · submitted 2026-05-22 · 💻 cs.CV

Learning a Particle Dynamics Model with Real-world Videos

Chanho Kim , Suhas V. Sumukh , Li Fuxin This is my paper

Pith reviewed 2026-05-25 04:45 UTC · model grok-4.3

classification 💻 cs.CV
keywords particle dynamicsGaussian splattingreal-world videosrendering supervisiondynamics predictionunsupervised learningobject interactionsrotation forecasting
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The pith

A particle dynamics model can be trained directly on unlabeled real-world videos by supervising predictions through differentiable rendering of dense Gaussians.

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

The paper sets out to learn how objects move and rotate in physical scenes using only ordinary video recordings. It represents each scene as a dense collection of particles that carry position, scale, and rotation information taken from a Gaussian splatting reconstruction. A neural network then forecasts the future positions and rotations of every particle. The only training signal comes from rendering the updated particles back into images and penalizing the difference from the next video frame. This setup removes the need for simulated environments, point tracks, or any direct labels on particle states.

Core claim

The central claim is that a particle-based dynamics model compatible with Gaussian splatting can be trained on real videos alone. The model receives dense particles that already encode scale and rotation, then predicts their position and rotation increments at each time step. Supervision occurs exclusively by rendering the forecasted particles into images and comparing them to the observed video frames, without any particle-level ground truth, correspondences, or subsampling of the Gaussian set.

What carries the argument

Particle dynamics predictor that ingests dense Gaussian-derived particles carrying scales and rotations and outputs their position and rotation changes, trained end-to-end by rendering supervision.

If this is right

  • Dynamics models become trainable on real footage instead of requiring synthetic data with perfect state information.
  • The method works with the full dense set of particles without any anchor-point subsampling.
  • Both translational and rotational motion are predicted within the same learned model.
  • A dataset of roughly 500 real videos of object interactions is released to support further study.
  • Learning proceeds without any requirement for labeled particle trajectories or point matches across frames.

Where Pith is reading between the lines

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

  • The same rendering-supervision loop could be tested on longer prediction horizons to measure how quickly errors accumulate.
  • Combining the learned particle predictor with other differentiable renderers might broaden the range of scenes that can be handled.
  • Robotic systems that observe only camera streams could use the trained model to anticipate future states of manipulated objects.
  • The approach invites direct comparison against physics engines on the released video set to quantify any remaining sim-to-real gap.

Load-bearing premise

Rendering supervision from video frames alone supplies enough signal to recover accurate particle dynamics and rotations without direct state labels, point correspondences, or heuristic subsampling.

What would settle it

If the dynamics model, when rolled forward and rendered, produces image sequences that diverge substantially from held-out real video frames of new object interactions, the claim that rendering alone suffices for learning would be refuted.

Figures

Figures reproduced from arXiv: 2605.23845 by Chanho Kim, Li Fuxin, Suhas V. Sumukh.

Figure 1
Figure 1. Figure 1: Example sequences illustrating the physical scenarios of interest. The dataset captures multi-object interactions with complex [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Objects used in our dataset. The falling-cube-stack sce [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Data collection setup with four Intel D455 RealSense [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: An overview of our data collection pipeline. It enables learning collision dynamics from real-world videos by providing two [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
read the original abstract

Data-driven learning approaches for physics simulation, sometimes referred to as world models, have emerged as promising alternatives to traditional physics simulators due to their differentiable nature. Prior work has demonstrated impressive results in predicting the motions of rigid and non-rigid objects in complex scenes involving multiple interacting bodies. However, these models are typically trained in simulated environments because obtaining perfect state information such as complete scene point clouds and point correspondences over time is challenging in real-world settings. This reliance on synthetic data can limit their applicability when the sim-to-real gap is large. In this work, we aim to overcome these limitations by introducing a novel framework for training neural object dynamics models directly from unlabeled real-world videos. Specifically, we propose to learn a particle-based dynamics model compatible with a Gaussian splatting framework, which operates on dense particles derived from Gaussians (i.e., particles with scales and rotations) and predicts their position and rotation changes over time. The model is trained via rendering supervision, enabling learning from real-world videos without requiring particle-level labeled states. Our model operates directly on dense Gaussians without relying on heuristic subsampling anchor points. To enable this study, we also present a real-world dataset consisting of about 500 videos capturing diverse object interactions.

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

1 major / 0 minor

Summary. The paper proposes a particle-based dynamics model compatible with Gaussian splatting that operates on dense particles (with scales and rotations) derived from Gaussians and predicts per-particle position and rotation changes over time. The model is trained end-to-end via rendering supervision on unlabeled real-world videos, without particle-level state labels or point correspondences, and the authors introduce a new dataset of approximately 500 videos of object interactions.

Significance. If the central claim holds, the work would enable training of differentiable world models directly on real video data, reducing dependence on simulated environments with perfect state information and potentially narrowing the sim-to-real gap. The release of a real-world video dataset of object interactions is a concrete positive contribution that could support follow-on research.

major comments (1)
  1. [Abstract] Abstract: the claim that rendering supervision alone supplies sufficient signal to recover accurate 3D position and rotation deltas for every dense Gaussian-derived particle is not supported by any derivation, loss formulation, or analysis in the provided manuscript. Because the model predicts deltas directly on the full unsampled set and receives no explicit 3D supervision or point tracks, the abstract leaves open the possibility that multiple incorrect dynamics produce visually plausible renderings.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and the opportunity to clarify the claims in our work. We address the major comment on the abstract below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that rendering supervision alone supplies sufficient signal to recover accurate 3D position and rotation deltas for every dense Gaussian-derived particle is not supported by any derivation, loss formulation, or analysis in the provided manuscript. Because the model predicts deltas directly on the full unsampled set and receives no explicit 3D supervision or point tracks, the abstract leaves open the possibility that multiple incorrect dynamics produce visually plausible renderings.

    Authors: We agree the abstract would benefit from greater precision. The manuscript formulates the training objective as an image-space rendering loss between predicted particle states (position/rotation deltas applied to dense Gaussians) and observed video frames, optimized end-to-end without 3D labels. However, we acknowledge the absence of a formal identifiability analysis or derivation showing that the recovered dynamics are unique rather than merely rendering-consistent. In revision we will (1) revise the abstract to state that the model learns dynamics consistent with observed renderings, and (2) add a short discussion section on potential ambiguities and the role of temporal consistency and the dense particle representation in mitigating them. This is a substantive clarification rather than a change to the method or experiments. revision: yes

Circularity Check

0 steps flagged

No circularity: dynamics model trained via independent rendering loss

full rationale

The paper's central derivation trains a particle dynamics model to predict per-particle position and rotation deltas directly from dense Gaussians, with supervision coming solely from a rendering loss on real video frames and no particle-level state labels or correspondences. This setup does not reduce the predicted deltas to the inputs by construction, nor does it rely on self-citations, fitted parameters renamed as predictions, or ansatzes smuggled from prior work. The abstract and description present a standard end-to-end learning pipeline where the loss signal is external to the model's forward predictions, making the chain self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no concrete information on free parameters, background axioms, or newly postulated entities; the particle representation and Gaussian splatting are presumed to draw from prior literature.

pith-pipeline@v0.9.0 · 5747 in / 1178 out tokens · 25553 ms · 2026-05-25T04:45:42.676395+00:00 · methodology

discussion (0)

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    This allows us to evaluate the results using rendering-based metrics, as presented in the main paper

    to each predicted object to recover dense Gaussians. This allows us to evaluate the results using rendering-based metrics, as presented in the main paper. E. Ablation on Different 4D Gaussian Genera- tion Methods We compare our approach with dynamic-scene GS meth- ods [16, 31], which can produce 3D Gaussian trajectories for both model input and supervisio...

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