ParticleGen: A Multi-Agent System for Particle Effects Generation

Abstract

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.