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Learning Physics-Grounded 4D Dynamics with Neural Gaussian Force Fields

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Physical reasoningvideo prediction
Abstract:

Predicting physical dynamics from raw visual data remains a major challenge in AI. While recent video generation models have achieved impressive visual quality, they still cannot consistently generate physically plausible videos due to a lack of modeling of physical laws. Recent approaches combining 3D Gaussian splatting and physics engines can produce physically plausible videos, but are hindered by high computational costs in both reconstruction and simulation, and often lack robustness in complex real-world scenarios. To address these issues, we introduce Neural Gaussian Force Field (NGFF), an end-to-end neural framework that integrates 3D Gaussian perception with physics-based dynamic modeling to generate interactive, physically realistic 4D videos from multi-view RGB inputs, achieving two orders of magnitude faster than prior Gaussian simulators. To support training, we also present GSCollision, a 4D Gaussian dataset featuring diverse materials, multi-object interactions, and complex scenes, totaling over 640k rendered physical videos (∼4 TB). Evaluations on synthetic and real 3D scenarios show NGFF’s strong generalization and robustness in physical reasoning, advancing video prediction towards physics-grounded world models.

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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.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces Neural Gaussian Force Field (NGFF), an end-to-end framework integrating 3D Gaussian perception with physics-based dynamics for interactive 4D video generation from multi-view RGB inputs. According to the taxonomy, this work resides in the 'Neural Physics Integration with Gaussian Representations' leaf under 'Physics-Based Dynamic Scene Modeling'. This leaf contains only two papers total, including the original work, indicating a relatively sparse and emerging research direction. The sibling paper explores Gaussian velocity modeling, suggesting this specific intersection of neural physics and Gaussian representations is not yet crowded.

The taxonomy reveals that the broader 'Physics-Based Dynamic Scene Modeling' branch contains three distinct leaves: neural-Gaussian integration, material-aware simulation, and physics-informed driving scene generation. Neighboring branches address geometry-aware synthesis (focusing on cross-view consistency without explicit physics) and physically-based rendering (emphasizing light transport rather than dynamics). The scope note for the parent branch explicitly excludes 'purely data-driven or geometric methods', positioning NGFF's physics-grounded approach as distinct from appearance-based video generation. The framework's force field modeling connects it to physics simulation while its Gaussian representation links to rendering-focused methods.

Among the three contributions analyzed, the literature search examined 22 candidates total. The NGFF framework itself was compared against 2 candidates with no refutations found. The GSCollision dataset examined 10 candidates with no overlapping prior work identified. However, the force field modeling via neural operators contribution examined 10 candidates and found 1 refutable match, suggesting some conceptual overlap exists in this specific technical component. Given the limited search scope of 22 papers, these statistics indicate the overall framework appears relatively novel, though certain modeling techniques may build on established neural operator approaches.

Based on the limited top-K semantic search conducted, the work appears to occupy a sparsely populated research direction at the intersection of Gaussian representations and neural physics. The single refutation among 22 candidates examined suggests incremental overlap in specific technical choices rather than wholesale duplication. However, the analysis does not cover exhaustive literature review across all physics simulation or neural rendering domains, leaving open the possibility of additional related work beyond the examined candidates.

Taxonomy

Core-task Taxonomy Papers
14
3
Claimed Contributions
22
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: physics-grounded 4D video prediction from multi-view RGB inputs. The field encompasses several major branches that address different facets of dynamic scene understanding and synthesis. Physics-Based Dynamic Scene Modeling integrates physical laws—such as forces, velocities, and material properties—into neural representations to enable realistic temporal evolution. Geometry-Aware Multi-View Video Synthesis focuses on leveraging geometric consistency across viewpoints to reconstruct and predict dynamic content, often relying on multi-view stereo or volumetric techniques. Physically-Based Rendering and Relighting aims to disentangle lighting and material properties for photorealistic re-rendering under novel illumination. Interactive Motion Editing and Control provides user-driven manipulation of dynamic scenes, while Specialized Multi-View Applications target domain-specific challenges like autonomous driving or sports analysis. Fast Generalizable Radiance Field Reconstruction emphasizes efficient, feed-forward methods that can quickly adapt to new scenes without per-scene optimization, exemplified by approaches like MVSNeRF[14]. Within Physics-Based Dynamic Scene Modeling, a particularly active line of work integrates neural physics with Gaussian-based representations to achieve both high-fidelity rendering and physically plausible dynamics. Neural Gaussian Force Fields[0] exemplifies this direction by embedding force and velocity fields directly into Gaussian primitives, enabling interactive simulation and prediction. This contrasts with neighboring methods such as FreeGave Gaussian Velocity[5], which also models velocity within Gaussian frameworks but may differ in how physical constraints are enforced or how multi-modal sensor data is incorporated. Meanwhile, works like Multi-modal 4D Simulation[3] and GenieDrive Physics World[8] explore broader integration of physics engines with learned representations, often targeting driving scenarios or complex multi-object interactions. The central challenge across these branches remains balancing computational efficiency, physical realism, and generalization to unseen dynamics, with Neural Gaussian Force Fields[0] positioned at the intersection of explicit Gaussian rendering and implicit neural physics modeling.

Claimed Contributions

Neural Gaussian Force Field (NGFF) framework

NGFF is an end-to-end neural framework that learns explicit force fields from 3D Gaussian representations to generate interactive, physically realistic 4D videos from multi-view RGB inputs. The framework combines feed-forward 3D Gaussian reconstruction with neural dynamics prediction through learned force fields integrated via ODE solvers, achieving computational efficiency while maintaining physical consistency.

2 retrieved papers
GSCollision dataset

GSCollision is a comprehensive 3D Gaussian-splats physical reasoning dataset totaling 640k rendered videos (approximately 4TB) that captures realistic behaviors of both rigid and deformable bodies. The dataset features 10 everyday objects with diverse material properties across 3,200 physically realistic scenarios, incorporating real-world backgrounds from WildRGBD to enhance visual complexity and realism.

10 retrieved papers
Force field modeling via neural operators

The framework formulates dynamic prediction as neural operator learning over explicit force fields, modeling both global transformation forces and local stress fields for deformable objects. This operator-based formulation on relational graphs enables unified modeling of rigid and soft body interactions while achieving robust generalization across spatial configurations, temporal horizons, and compositional variations.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Neural Gaussian Force Field (NGFF) framework

NGFF is an end-to-end neural framework that learns explicit force fields from 3D Gaussian representations to generate interactive, physically realistic 4D videos from multi-view RGB inputs. The framework combines feed-forward 3D Gaussian reconstruction with neural dynamics prediction through learned force fields integrated via ODE solvers, achieving computational efficiency while maintaining physical consistency.

Contribution

GSCollision dataset

GSCollision is a comprehensive 3D Gaussian-splats physical reasoning dataset totaling 640k rendered videos (approximately 4TB) that captures realistic behaviors of both rigid and deformable bodies. The dataset features 10 everyday objects with diverse material properties across 3,200 physically realistic scenarios, incorporating real-world backgrounds from WildRGBD to enhance visual complexity and realism.

Contribution

Force field modeling via neural operators

The framework formulates dynamic prediction as neural operator learning over explicit force fields, modeling both global transformation forces and local stress fields for deformable objects. This operator-based formulation on relational graphs enables unified modeling of rigid and soft body interactions while achieving robust generalization across spatial configurations, temporal horizons, and compositional variations.

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