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DiffWind: Physics-Informed Differentiable Modeling of Wind-Driven Object Dynamics

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Physics-based Modeling3D DynamicsSystem IdentificationDifferentiable Physics
Abstract:

Modeling wind-driven object dynamics from video observations is highly challenging due to the invisibility and spatio–temporal variability of wind, as well as the complex deformations of objects. We present DiffWind, a physics-informed differentiable framework that unifies wind–object interaction modeling, video-based reconstruction, and forward simulation. Specifically, we represent wind as a grid-based physical field and objects as particle systems derived from 3D Gaussian Splatting, with their interaction modeled by the Material Point Method (MPM). To recover wind-driven object dynamics, we introduce a reconstruction framework that jointly optimizes the spatio–temporal wind force field and object motion through differentiable rendering and simulation. To ensure physical validity, we incorporate the Lattice Boltzmann Method (LBM) as a physics-informed constraint, enforcing compliance with fluid dynamics laws. Beyond reconstruction, our method naturally supports forward simulation under novel wind conditions and enable new applications such as wind retargeting. We further introduce WD-Objects, a dataset of synthetic and real-world wind-driven scenes. Extensive experiments demonstrate that our method significantly outperforms prior dynamic scene modeling approaches in both reconstruction accuracy and simulation fidelity, opening a new avenue for video-based wind–object interaction modeling.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces DiffWind, a differentiable framework that jointly optimizes wind fields and object motion from video using physics-informed constraints. It resides in the 'Differentiable Physics-Based Frameworks' leaf, which contains only three papers total, indicating a relatively sparse research direction within the broader taxonomy of 50 papers. This leaf sits under 'Physics-Informed Reconstruction and Simulation', distinguishing itself from purely data-driven or sensor-based approaches by explicitly integrating physical laws (MPM, LBM) into the reconstruction pipeline.

The taxonomy reveals neighboring research directions that contextualize this work. The sibling leaf 'Visual Anemometry and Force Inference' (5 papers) focuses on inferring wind properties without full reconstruction, while 'Forward Simulation and Visualization' (3 papers) addresses the inverse problem—simulating dynamics given known wind. Nearby branches include 'Data-Driven Prediction and Analysis' (9 papers across neural prediction and motion tracking) and 'Measurement and Tracking Systems' (12 papers emphasizing sensor integration). DiffWind bridges reconstruction and simulation by enabling both inverse recovery and forward prediction under novel conditions, a capability less emphasized in neighboring leaves.

Among 16 candidates examined, the contribution-level analysis reveals mixed novelty signals. The core DiffWind framework (Contribution 1) examined 9 candidates with no clear refutations, suggesting limited direct overlap in the search scope. However, the differentiable reconstruction framework with physics constraints (Contribution 2) found 1 refutable candidate among 1 examined, indicating potential prior work in this specific technical approach. The WD-Objects dataset (Contribution 3) examined 6 candidates without refutation, though dataset novelty depends heavily on domain coverage not fully captured by semantic search.

Based on the limited search scope of 16 top-K semantic matches, the work appears to occupy a sparsely populated niche combining differentiable rendering, particle-based simulation, and fluid dynamics constraints. The taxonomy structure confirms that differentiable physics-based reconstruction remains an emerging area with few direct comparators. However, the analysis cannot rule out relevant prior work outside the examined candidates, particularly in adjacent computer graphics or computational fluid dynamics communities not fully represented in this taxonomy.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
16
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Modeling wind-driven object dynamics from video observations. The field encompasses diverse approaches organized into four main branches. Physics-Informed Reconstruction and Simulation emphasizes integrating physical principles—such as differentiable physics engines and force-based modeling—to recover wind fields and object motion from visual data, as seen in works like DiffWind[0] and Force Prompting[3]. Data-Driven Prediction and Analysis focuses on learning-based methods that extract patterns from video without explicit physics models, often leveraging neural networks for tasks like motion prediction or wind estimation (e.g., See the Wind[14], Cyclenet Cinemagraphs[15]). Measurement and Tracking Systems develop specialized instrumentation and computer vision pipelines to quantify motion in challenging environments, including maritime settings (Maritime UAV Landing[5]) and agricultural contexts (Pear Leaf Dynamics[2], Visual Anemometry[9]). Domain-Specific Applications address targeted problems such as wildfire behavior (Firebrand Tracking[4]), structural monitoring (Illumination Pole Monitoring[6]), and ecological phenomena (Albatross Flight Behavior[12]), where wind-object interactions are critical. A particularly active line of work explores differentiable physics-based frameworks that enable end-to-end optimization of wind parameters and material properties from video. DiffWind[0] exemplifies this trend by coupling neural rendering with physics simulation to infer wind forces acting on deformable objects, positioning itself alongside Force Prompting[3], which similarly integrates force estimation into visual reasoning. In contrast, Cloth in Wind[17] takes a more classical simulation approach, focusing on realistic cloth dynamics under prescribed wind conditions rather than inverse inference. These physics-informed methods face trade-offs between model fidelity and computational cost, while data-driven alternatives sacrifice interpretability for flexibility. Open questions remain around generalizing across object types, handling occlusions, and validating inferred wind fields against ground-truth measurements—challenges that bridge the physics-informed and measurement-focused branches of the taxonomy.

Claimed Contributions

DiffWind: Physics-informed differentiable framework for wind-object interaction modeling

The authors propose a unified framework that represents wind as a grid-based physical field and objects as particle systems derived from 3D Gaussian Splatting, with their interaction modeled by the Material Point Method (MPM). This enables joint reconstruction of wind fields and object dynamics from videos, forward simulation under novel wind conditions, and wind retargeting applications.

9 retrieved papers
Differentiable reconstruction framework with physics-informed constraints

The authors develop a differentiable inverse reconstruction framework that simultaneously recovers dynamic object motion and invisible wind force fields from sparse-view videos. They incorporate the Lattice Boltzmann Method (LBM) as a physics-informed constraint to enforce compliance with fluid dynamics laws during optimization.

1 retrieved paper
Can Refute
WD-Objects dataset for wind-driven scene evaluation

The authors construct a new dataset covering both synthetic and real-world wind-driven object scenes to enable comprehensive evaluation of wind-object interaction modeling methods, as no publicly available datasets existed for this task.

6 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

DiffWind: Physics-informed differentiable framework for wind-object interaction modeling

The authors propose a unified framework that represents wind as a grid-based physical field and objects as particle systems derived from 3D Gaussian Splatting, with their interaction modeled by the Material Point Method (MPM). This enables joint reconstruction of wind fields and object dynamics from videos, forward simulation under novel wind conditions, and wind retargeting applications.

Contribution

Differentiable reconstruction framework with physics-informed constraints

The authors develop a differentiable inverse reconstruction framework that simultaneously recovers dynamic object motion and invisible wind force fields from sparse-view videos. They incorporate the Lattice Boltzmann Method (LBM) as a physics-informed constraint to enforce compliance with fluid dynamics laws during optimization.

Contribution

WD-Objects dataset for wind-driven scene evaluation

The authors construct a new dataset covering both synthetic and real-world wind-driven object scenes to enable comprehensive evaluation of wind-object interaction modeling methods, as no publicly available datasets existed for this task.

Table of Contents