{"ID":2890075,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.19782","arxiv_id":"2507.19782","title":"KinemaFX: A Kinematic-Driven Interactive System for Particle Effect Exploration and Customization","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.","short_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 a...","url_abs":"https://arxiv.org/abs/2507.19782","url_pdf":"https://arxiv.org/pdf/2507.19782v1","authors":"[\"Yifei Zhang\",\"Lin-Ping Yuan\",\"Yuheng Zhao\",\"Jielin Feng\",\"Siming Chen\"]","published":"2025-07-26T04:11:46Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[\"Large Language Model\",\"Language Model\",\"LoRA\"]","has_code":false}