arXiv:2606.15015v1 Announce Type: cross 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. Ins
Advances in neural fields and physics-based modeling are converging, enabling more sophisticated and compute-intensive approaches to real-time physics simulations crucial for next-generation AI applications.
This development is crucial for AI systems to accurately understand and interact with the physical world, especially in complex, contact-rich environments, which is a prerequisite for advanced robotics and physically-grounded generative AI.
The ability to model robust, physically consistent 3D object dynamics leveraging neural energy fields promises more realistic simulations and, consequently, more capable AI systems for tasks requiring physical interaction.
- · Robotics companies
- · Generative AI developers
- · Simulation software providers
- · Gaming and VR/AR industry
Improved physical interaction capabilities for AI models and robotic systems.
Faster development and safer deployment of autonomous systems in real-world, dynamic environments.
Potential for new classes of AI applications that require high-fidelity physical interaction, blurring the lines between digital and physical simulation.
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Read at arXiv cs.AI