Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation

2024-11-21

arXiv.org

AI Summary

Existing dynamic scene physics simulation methods are constrained by simplistic material modeling and limited tunable parameters, hindering accurate representation of complex real-world material behaviors—such as viscoelasticity, fracture, and fluid–solid coupling. To address this, we propose the first 4D dynamic simulation framework integrating multimodal foundation models with optical-flow-guided video diffusion. Our method employs image-based queries to drive material identification and initial parameter estimation, combines 3D Gaussian Splatting for scene representation with differentiable Material Point Method (MPM) for physical evolution, and introduces a novel co-optimization strategy between MPM and video diffusion—eliminating reliance on rendering losses or Score Distillation Sampling (SDS). This enables end-to-end learning of physically meaningful material parameters. Extensive experiments demonstrate substantial improvements in simulation fidelity, user controllability, and cross-material generalization under complex interactions and real-world scenarios.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multimodal Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Realistic simulation of dynamic scenes requires accurately capturing diverse material properties and modeling complex object interactions grounded in physical principles. However, existing methods are constrained to basic material types with limited predictable parameters, making them insufficient to represent the complexity of real-world materials. We introduce PhysFlow, a novel approach that leverages multi-modal foundation models and video diffusion to achieve enhanced 4D dynamic scene simulation. Our method utilizes multi-modal models to identify material types and initialize material parameters through image queries, while simultaneously inferring 3D Gaussian splats for detailed scene representation. We further refine these material parameters using video diffusion with a differentiable Material Point Method (MPM) and optical flow guidance rather than render loss or Score Distillation Sampling (SDS) loss. This integrated framework enables accurate prediction and realistic simulation of dynamic interactions in real-world scenarios, advancing both accuracy and flexibility in physics-based simulations.

Problem

• Enhance 4D dynamic scene simulation accuracy
• Overcome limitations in material property representation
• Improve realism in physics-based dynamic interactions

Innovation

• Leverages multi-modal models for material identification
• Uses video diffusion with MPM for refinement
• Integrates 3D Gaussian splats for detailed scenes