PhysMAS: Physics-Grounded Multi-Agent Synthesis of Compositional 4D Gaussians

• URL: http://arxiv.org/abs/2609.07174v1
• Date: Mon, 07 Sep 2026 08:07:05 GMT
• Title: PhysMAS: Physics-Grounded Multi-Agent Synthesis of Compositional 4D Gaussians
• Abstract summary: PhysMAS is a physics-grounded multi-agent framework for dynamic scene generation.<n>It invokes solver-aware skills to bind identities and profiles to per-particle MPM fields and execute all objects in a shared domain.<n>It achieves better semantic alignment and perceived physical plausibility while requiring less runtime.
• Score: 34.993124532814186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient, fully automatic, and physically plausible 4D Gaussian synthesis is an important goal for dynamic scene generation. Recent physics-based methods couple 3D Gaussians with the Material Point Method (MPM) to generate physically driven motion, but extending this paradigm to heterogeneous multi-part objects and interacting multi-object scenes remains challenging. Object-level physical assignment collapses distinct parts into a single material state, while one-shot predictions from large language models, vision-language models, or agents neither reliably bind different materials to identified parts nor verify that the resulting MPM configuration is executable. Score Distillation Sampling (SDS)-based parameter optimization, meanwhile, requires repeated per-scene score evaluations and gradient backpropagation, incurring lengthy optimization and potentially yielding suboptimal or unstable solutions. We therefore present PhysMAS, a physics-grounded multi-agent framework. From a motion prompt and four scene views, an Object-Part Scene Agent establishes persistent identities and calls a Material Reasoning Agent for part-wise profiles. It invokes solver-aware skills to bind these identities and profiles to per-particle MPM fields and execute all objects in a shared domain; the framework then screens candidate forward-simulation results. This supports heterogeneous multi-part and interacting multi-object scenes without per-scene diffusion-score backpropagation. Extensive experiments demonstrate that, compared with recent physics-based 4D Gaussian baselines that rely on SDS, PhysMAS achieves better semantic alignment and perceived physical plausibility while requiring less runtime.

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