NEXUS: Neural Energy Fields for Physically Consistent Contact-Rich 3D Object Dynamics
NEXUS uses neural energy fields for physically consistent contact-rich 3D object dynamics, improving long-horizon accuracy.
Key Findings
Methodology
NEXUS employs a neural energy-field framework, representing each object as a structural graph and constructing dynamic contact graphs. It formulates motion through scalar energy and dissipation terms rather than directly predicting states or accelerations. Conservative effects like gravity and elastic deformation are composed as additive energy terms, while non-conservative effects such as damping and impact-induced energy loss are modeled with learned Rayleigh-style dissipation.
Key Results
- In single-object dynamics prediction, NEXUS achieves the best long-horizon rollout error RE@48 of 0.137, significantly outperforming baseline methods.
- In multi-object scenarios, NEXUS shows the lowest RE@48 error of 0.185 in five-object contact scenes, demonstrating its superiority in complex contact scenarios.
- In video generation experiments, NEXUS outperforms other methods in semantic adherence and physical commonsense scoring.
Significance
NEXUS holds significant implications for academia and industry by addressing the challenge of unifying conservative and non-conservative effects in contact-rich 3D scenes. By providing a stable and controllable physics reasoning module, NEXUS enhances long-horizon accuracy and improves the physical plausibility of downstream physics-consistent video generation.
Technical Contribution
NEXUS's technical contributions lie in applying Hamiltonian neural networks' energy-dissipation dynamics formulation to contact-rich 3D dynamic scenes. Through graph-based representation and learned energy-dissipation functions, NEXUS surpasses existing learned and physics-structured dynamics baselines in long-horizon accuracy.
Novelty
NEXUS is the first to apply energy-dissipation dynamics formulation to contact-rich 3D dynamic scenes, innovatively combining structural and dynamic contact graphs to provide a unified physical interface for handling conservative and non-conservative effects.
Limitations
- The computational complexity in constructing contact graphs may lead to high memory consumption.
- The static environment assumption may limit applications in dynamic environments.
Future Work
Future work may include optimizing the computational efficiency of contact graph construction, exploring applications in dynamic environments, and further improving the visual quality of video generation.
AI Executive Summary
In physics-consistent video generation, existing methods often struggle to unify conservative and non-conservative effects in contact-rich 3D scenes. NEXUS addresses this issue through a neural energy-field framework. It represents each object as a structural graph and constructs dynamic contact graphs, using scalar energy and dissipation terms to describe motion. Experimental results show that NEXUS significantly outperforms baseline methods in long-horizon accuracy and enhances physical plausibility in video generation.
The core technical principles of NEXUS include using Hamiltonian neural networks' energy-dissipation dynamics formulation, providing a unified physical interface to handle conservative and non-conservative effects through graph-based representation and learned energy-dissipation functions. Experimental results demonstrate NEXUS's superior performance in both single-object and multi-object scenarios, particularly in complex contact scenes.
Despite the significant advancements in long-horizon accuracy and physical plausibility, NEXUS's computational complexity in contact graph construction needs optimization. Future research directions include improving computational efficiency and exploring applications in dynamic environments.
Deep Analysis
Background
Physics-consistent video generation requires controllable 3D object dynamics that remain physically consistent under contact, deformation, and external forcing. Existing methods often struggle to unify conservative and non-conservative effects in contact-rich 3D scenes, leading to insufficient physical consistency. NEXUS addresses this issue through a neural energy-field framework.
Core Problem
Existing trajectory generation methods often struggle to unify conservative and non-conservative effects in contact-rich 3D scenes, leading to insufficient physical consistency. This issue is particularly important in downstream applications like robotics, where physics-consistent rollouts are crucial.
Innovation
NEXUS uses a neural energy-field framework, representing each object as a structural graph and constructing dynamic contact graphs. It uses scalar energy and dissipation terms to describe motion, innovatively combining structural and dynamic contact graphs to provide a unified physical interface for handling conservative and non-conservative effects.
Methodology
- �� NEXUS represents each object as a structural graph and constructs dynamic contact graphs.
- �� It uses scalar energy and dissipation terms to describe motion rather than directly predicting states or accelerations.
- �� Conservative effects like gravity and elastic deformation are composed as additive energy terms.
- �� Non-conservative effects like damping and impact-induced energy loss are modeled with learned Rayleigh-style dissipation.
Experiments
The experimental design includes tests on single-object dynamics prediction and multi-object contact scenarios. Baseline methods include Explicit KNN Energy, Direct-State prediction, and Direct-Acceleration prediction. The primary evaluation metric is long-horizon rollout error RE@H.
Results
In single-object dynamics prediction, NEXUS achieves the best long-horizon rollout error RE@48 of 0.137. In multi-object scenarios, NEXUS shows the lowest RE@48 error of 0.185 in five-object contact scenes. In video generation experiments, NEXUS outperforms other methods in semantic adherence and physical commonsense scoring.
Applications
NEXUS can be used in downstream applications requiring physics-consistent rollouts, such as robotics and physics-consistent video generation. Its superiority in complex contact scenarios makes it widely applicable in these fields.
Limitations & Outlook
NEXUS's computational complexity in contact graph construction may lead to high memory consumption. Additionally, the static environment assumption may limit applications in dynamic environments. Future work may include optimizing computational efficiency and exploring applications in dynamic environments.
Plain Language Accessible to non-experts
Imagine a kitchen where NEXUS is like a smart chef. Each ingredient (object) has its own position and properties (structural graph), and the chef needs to decide how to cook based on the interactions between ingredients (contact graph). Conservative effects are like gravity and elastic deformation, the natural properties of ingredients, while non-conservative effects like damping and impact energy loss are changes that occur during cooking. NEXUS learns these changes to ensure each dish (3D dynamics) maintains consistent flavor (physical consistency) over time.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super cool game where characters can move freely in a 3D world. NEXUS is like the game's brain, making sure characters follow the rules of physics when they jump, collide, and bounce. It's like a smart robot predicting every move, making the game look more real! Isn't that awesome?
Glossary
Neural Energy Fields
A neural network-modeled energy field used to describe the dynamic behavior of objects.
NEXUS uses neural energy fields to unify the description of conservative and non-conservative effects.
Structural Graph
A graph representing the geometric structure of an object, used to model internal relationships.
NEXUS represents each object as a structural graph to capture its geometric properties.
Dynamic Contact Graph
A graph used to describe interactions between objects and between objects and the environment.
NEXUS constructs dynamic contact graphs during rollouts to capture contact behavior.
Rayleigh-style Dissipation
A dissipation function used to model non-conservative effects, capturing damping and impact energy loss.
NEXUS uses Rayleigh-style dissipation to describe non-conservative effects.
Long-horizon Rollout Error
A metric used to evaluate a model's accuracy in long-term predictions.
NEXUS outperforms baseline methods in long-horizon rollout error evaluation.
Open Questions Unanswered questions from this research
- 1 How to optimize the computational efficiency of contact graph construction in dynamic environments?
- 2 How to further improve the visual quality of video generation?
Applications
Immediate Applications
Robotic Motion Planning
NEXUS can be used for robotic motion planning, providing physics-consistent path predictions to enhance navigation in complex environments.
Long-term Vision
Enhanced Realism in Virtual Reality
NEXUS can enhance physical realism in virtual reality, providing a more natural user experience.
Abstract
Physics-grounded video generation requires controllable 3D object dynamics that remain physically consistent under contact, deformation, and external forcing. Existing trajectory-based methods often model isolated physical effects, making it difficult to compose conservative and non-conservative dynamics in contact-rich 3D scenes. We present NEXUS, a neural energy-field framework for contact-rich 3D object dynamics. NEXUS represents each object as a structural graph and constructs dynamic object-object and object-environment contact graphs. Inspired by Hamiltonian Neural Networks, NEXUS formulates motion through scalar energy and dissipation terms rather than directly predicting states or accelerations. Conservative effects, including gravity and elastic deformation, are composed as additive energy terms, while non-conservative effects such as damping and impact-induced energy loss are modeled with learned Rayleigh-style dissipation. Forces are derived by differentiating the energy and dissipation functions and rolled out with a multi-substep semi-implicit integrator. Across controlled trajectory benchmarks, NEXUS improves long-horizon accuracy over representative learned and physics-structured dynamics baselines under varying mechanical properties and physical-effect compositions. We further show that NEXUS trajectories provide effective guidance for contact-rich video generation, improving physical plausibility while maintaining competitive visual quality.