Achieving fully automated, physically plausible 3D motion synthesis is a core objective in graphics and generative AI. However, configuring complex environmental force fields still relies entirely on manual expert intervention, creating a severe bottleneck for large-scale simulation data generation. Existing automated methods primarily focus on material optimization and exhibit severe modality gaps and technical flaws when applied to the vastly more complex force field optimization space: naive Large Language Models (LLMs) lack underlying simulation feedback, causing severe physical inaccuracies, while traditional Score Distillation Sampling (SDS) suffers from sluggish gradients, local optima entrapment, and a mathematical inability to dynamically switch discrete force fields. To address this, we propose PhysAgent, the first simulator-in-the-loop multi-agent framework that leverages multimodal inputs for automated, physically grounded 4D synthesis. By decoupling intrinsic materials from extrinsic dynamics, PhysAgent utilizes a Semantic Agent equipped with an externalized Force Field Skill module to master simulation rules and generate valid initializations. Subsequently, the Refine Agents, driven by Trajectory-Grounded Multi-Agent Feedback, leverage vision foundation models to extract dense point trajectories from rendered frames. By converting these explicit motion trajectories into structured textual descriptors, the agent harnesses LLM commonsense reasoning to execute zero-shot macroscopic leaps, effectively escaping local optima and dynamically switching discrete force fields. Extensive experiments demonstrate that PhysAgent rapidly generates stable, diverse physical scenes from arbitrary multimodal prompts, significantly outperforming existing baselines in both generation diversity and physical accuracy.

翻译：实现全自动且物理可信的三维运动合成是图形学与生成式人工智能的核心目标之一。然而，复杂环境力场的配置仍完全依赖人工专家干预，这为大规模仿真数据生成造成了严重瓶颈。现有自动化方法主要聚焦于材料优化，在应用于复杂度更高的力场优化空间时暴露出严重的模态鸿沟与技术缺陷：简单化的大型语言模型缺乏底层仿真反馈，导致严重的物理不准确性；而传统得分蒸馏采样则存在梯度迟滞、局部最优陷阱，且在数学上无法实现离散力场的动态切换。为此，我们提出PhysAgent——首个将仿真器置于环路中的多智能体框架，通过多模态输入实现自动化、基于物理的四维合成。通过解耦内在材料与外在动力学，PhysAgent利用配备外部化力场技能模块的语义智能体掌握仿真规则并生成有效初始化。随后，由轨迹驱动多智能体反馈驱动的精炼智能体借助视觉基础模型从渲染帧中提取密集点轨迹。通过将这些显式运动轨迹转化为结构化文本描述符，该智能体利用大语言模型的常识推理执行零样本宏观跃迁，有效摆脱局部最优并动态切换离散力场。大量实验表明，PhysAgent能根据任意多模态提示快速生成稳定多样的物理场景，在生成多样性与物理准确性上显著超越现有基线。