GS-Agent把多个AI智能体和物理引擎连起来，你只要说句话就能生成带物理模拟的4D场景，液体、物体碰撞都能搞定，做内容创作或模拟都省力。
GS-Agent是一个端到端多智能体框架，集成了物理引擎，能从自然语言描述生成动态且物理真实的4D世界。该框架将任务分解为实体管理（3D资产、材质、放置、运动控制）和渲染配置（相机、灯光），多个智能体通过代码与物理引擎交互并迭代构建。实验结果表明，GS-Agent能有效转换自然语言，生成包含液体、可变形物体和刚体丰富交互的4D世界，同时实现电影级相机和灯光控制。项目页面已公开。
GS-Agent: Creating 4D Physical Worlds With Generative Simulation
Creating dynamic and physically realistic 4D worlds from natural language descriptions is both fascinating and challenging. Traditional computer graphics methods rely on manual creation, requiring extensive human effort to fine-tune materials, motions, and visual fidelity. Recent advances in generative foundation models have sparked interest in learning to generate such 4D worlds from large-scale data; however, existing methods still struggle to ensure physical plausibility and controllability. In this work, we take a different path by leveraging foundation models to construct an agentic system that emulates how humans traditionally create 4D worlds, yet automates the entire process. We present GS-Agent, an end-to-end multi-agent framework that integrates physics engines in the loop to generate realistic, dynamic, and controllable 4D physical worlds from natural language. Inspired by how humans build 4D worlds, GS-Agent decomposes the task into entity management, covering 3D asset curation, material tuning, placement, and motion control, and rendering configuration, including camera and lighting manipulation. Multiple agents with distinct expertise interact with the physics engine via code, seek multimodal feedback, and collaborate to iteratively construct 4D worlds that align with the given descriptions. Experimental results show that GS-Agent effectively converts natural language into diverse and physically plausible 4D worlds exhibiting rich interactions among liquids, deformable objects, and rigid bodies, while achieving cinematic camera and lighting control. We envision GS-Agent as a foundation for a new paradigm in 4D world generation, empowering creative content creation and physical AI. Project page at https://umass-embodied-agi.github.io/gs-agent/