Efficient Physics Simulation for 3D Scenes via MLLM-Guided Gaussian Splatting (2411.12789v2)

Abstract: Recent advancements in 3D generation models have opened new possibilities for simulating dynamic 3D object movements and customizing behaviors, yet creating this content remains challenging. Current methods often require manual assignment of precise physical properties for simulations or rely on video generation models to predict them, which is computationally intensive. In this paper, we rethink the usage of multi-modal LLM (MLLM) in physics-based simulation, and present Sim Anything, a physics-based approach that endows static 3D objects with interactive dynamics. We begin with detailed scene reconstruction and object-level 3D open-vocabulary segmentation, progressing to multi-view image in-painting. Inspired by human visual reasoning, we propose MLLM-based Physical Property Perception (MLLM-P3) to predict mean physical properties of objects in a zero-shot manner. Based on the mean values and the object's geometry, the Material Property Distribution Prediction model (MPDP) model then estimates the full distribution, reformulating the problem as probability distribution estimation to reduce computational costs. Finally, we simulate objects in an open-world scene with particles sampled via the Physical-Geometric Adaptive Sampling (PGAS) strategy, efficiently capturing complex deformations and significantly reducing computational costs. Extensive experiments and user studies demonstrate our Sim Anything achieves more realistic motion than state-of-the-art methods within 2 minutes on a single GPU.

• The paper introduces MLLM-P3 to zero-shot predict physical properties via visual reasoning, transforming static 3D objects into interactive entities.
• It pioneers a probabilistic approach to physical property estimation that enhances simulation realism while reducing computational costs.
• The method employs Physical-Geometric Adaptive Sampling to dynamically optimize particle distribution for accurate deformation and boundary capture.

Sim Anything: Automated 3D Physical Simulation of Open-world Scenes with Gaussian Splatting

The paper "Sim Anything" presents a novel approach for simulating realistic 3D object dynamics within open-world scenes. Leveraging recent advancements in 3D generation models, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), the authors propose a framework that automates the physical simulation of static 3D objects, transforming them into interactive entities that can respond to arbitrary forces. The introduction of Gaussian Splatting marks a significant methodological advancement, facilitating the efficient modeling and rendering of point cloud-based 3D scenes while incorporating physical phenomena.

Key Contributions

1. Reformulation of Physical Property Estimation: The authors introduce a Multi-modal LLM-based Physical Property Perception (MLLM-P3) to zero-shot predict the mean physical properties of objects using visual reasoning. This reformulation addresses the computational inefficiencies present in prior models reliant on video data, which often exhibit significant overhead and limited adaptability.
2. Probabilistic Distribution for Material Properties: Unlike traditional deterministic approaches, the paper pioneers the conceptualization of physical property estimation as a probability distribution problem. By predicting the full range of these properties and incorporating geometric features, the approach reduces computational costs and enhances simulation realism.
3. Innovative Sampling Strategies: The integration of Physical-Geometric Adaptive Sampling (PGAS) effectively optimizes particle sampling by dynamically adjusting based on an object's material properties and geometric complexity. This strategy ensures precise boundary capture and accurate deformation simulation, particularly for objects of varying rigidity.

Experimental Results

The experimental validation conducted using both real-world and synthetic datasets demonstrates the efficacy of Sim Anything. The proposed framework outperforms state-of-the-art methods in terms of motion realism and aesthetic quality, as evidenced by higher realism and aesthetic scores in user studies. Sim Anything's capability to generate realistic and consistent motion in diverse scenarios, accompanied by shorter inference times on standard GPUs, is a testament to its methodological robustness and computational efficiency.

Practical Implications and Theoretical Advancements

Sim Anything's contributions extend beyond theoretical innovation, bearing significant ramifications for practical applications in areas like virtual reality, robotics, and embodied intelligence. The ability to automate the estimation of physical properties and simulate detailed interactions in diverse environments extends the potential for seamless integration into real-time systems and applications necessitating dynamic and adaptive 3D interactions.

Speculation on Future Directions

Moving forward, research might explore the integration of generative models to further enhance the ability to reconstruct occluded scenes and objects. Such improvements could address the current limitations related to segmenting occluded components, potentially catalyzing advancements in creating more immersive and interactive virtual environments. Moreover, expanding this framework's applicability to larger scale simulations or integrating with additional sensory data could enrich the fidelity and interactivity of virtual simulations.

In sum, "Sim Anything" represents a sophisticated approach to automated 3D simulation, combining theoretical advancements with practical optimizations to drive progress in realistic 3D interaction modeling and rendering. As computational resources continue to evolve, further refinements and extensions of this framework are anticipated, paving the way for increasingly dynamic and interactive virtual experiences.