AI 中文总结
本研究提出多智能体框架ParticleGen，可从自然语言描述合成结构化可编辑粒子系统，经Unreal Engine 5的Niagara系统多场景验证，能降低创作门槛、提升迭代效率。
AI 中文摘要
粒子系统广泛应用于数字娱乐领域，用于创建动态场景元素和视觉效果。然而，创作高质量的粒子效果仍十分耗时费力，且需要专业知识，要求从业者掌握复杂的程序规则和高维参数空间。近期的大型语言模型（LLM）支持用户通过自然语言指定粒子效果，但将高层创意意图可靠地转化为可执行的程序逻辑和低层参数仍然困难。本研究中，我们提出一种多智能体框架，用于从零开始根据自然语言描述合成结构化且可编辑的粒子系统。给定文本提示，该框架首先通过解耦的规划和参数化流水线生成初始粒子配置，随后基于渲染反馈迭代优化结果。为支持精准的针对性调整，我们进一步引入诊断机制，将观察到的视觉瑕疵与其底层程序原因关联起来。我们在Unreal Engine 5的Niagara系统中，通过多种场景验证了我们的方法，包括元素法术、动态自然现象和烟花。定量与定性评估表明，我们的方法实现了高语义保真度和视觉质量。通过直接合成结构化的粒子模拟逻辑，该框架降低了粒子效果创作的技术门槛，提升了创意迭代的效率。
英文摘要
Particle systems are widely used in digital entertainment to create dynamic scene elements and visual effects. However, authoring high-quality particle effects remains labor-intensive and demands specialized expertise, requiring practitioners to navigate complex procedural rules and high-dimensional parameter spaces. Recent large language models (LLMs) enable users to specify particle effects through natural language, yet reliably translating high-level creative intent into executable procedural logic and low-level parameters remains difficult. In this work, we present a multi-agent framework for the from-scratch synthesis of structured and editable particle systems from natural language descriptions. Given a text prompt, our framework first generates an initial particle configuration through a decoupled planning and parameterization pipeline, and then iteratively improves the result based on rendered feedback. To support precise and targeted adjustments, we further introduce a diagnostic mechanism that links observed visual artifacts to their underlying procedural causes. We validate our approach in Unreal Engine 5's Niagara system across a diverse set of scenarios, including elemental spells, dynamic natural phenomena, and fireworks. Quantitative and qualitative evaluations show that our method achieves high semantic fidelity and visual quality. By directly synthesizing structured particle simulation logic, our framework reduces the technical barrier to particle effect authoring and improves the efficiency of creative iteration.
