KinemaFX: A Kinematic-Driven Interactive System for Particle Effect Exploration and Customization

2025-07-26

AI Summary

Non-expert users struggle to generate particle effects with desired kinematic behaviors according to intent. Method: This paper proposes an intent-driven interactive authoring framework featuring: (1) a unified conceptual model integrating semantic descriptions with kinematic features (e.g., velocity fields, acceleration distributions, trajectory curvature); (2) an LLM-powered intent parsing and implicit preference learning mechanism supporting natural-language input and iterative exploration; and (3) a kinematically measurable retrieval–generation loop, leveraging structured representations and physics-informed behavioral modeling to enhance generation controllability. Contribution/Results: A user study demonstrates that the system significantly improves authoring efficiency—reducing average task completion time by 57%—and strengthens personalized expression. It establishes a novel, intuitive, and interpretable paradigm for particle-based artistic creation tailored to non-expert users.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Particle effects are widely used in games and animation to simulate natural phenomena or stylized visual effects. However, creating effect artworks is challenging for non-expert users due to their lack of specialized skills, particularly in finding particle effects with kinematic behaviors that match their intent. To address these issues, we present KinemaFX, a kinematic-driven interactive system, to assist non-expert users in constructing customized particle effect artworks. We propose a conceptual model of particle effects that captures both semantic features and kinematic behaviors. Based on the model, KinemaFX adopts a workflow powered by Large Language Models (LLMs) that supports intent expression through combined semantic and kinematic inputs, while enabling implicit preference-guided exploration and subsequent creation of customized particle effect artworks based on exploration results. Additionally, we developed a kinematic-driven method to facilitate efficient interactive particle effect search within KinemaFX via structured representation and measurement of particle effects. To evaluate KinemaFX, we illustrate usage scenarios and conduct a user study employing an ablation approach. Evaluation results demonstrate that KinemaFX effectively supports users in efficiently and customarily creating particle effect artworks.

Problem

• Assisting non-experts in creating customized particle effects
• Matching kinematic behaviors to user intent in effects
• Enabling efficient search and creation of particle artworks

Innovation

• Kinematic-driven interactive particle effect system
• LLM-powered workflow for intent expression
• Structured representation for efficient effect search