Text animation, a foundational element in video creation, enables efficient and cost-effective communication, thriving in advertisements, journalism, and social media. However, traditional animation workflows present significant usability barriers for non-professionals, with intricate operational procedures severely hindering creative productivity. To address this, we propose a Large Language Model (LLM)-aided text animation editing system that enables real-time intent tracking and flexible editing. The system introduces an agent-based dual-stream pipeline that integrates context-aware inline suggestions and conversational guidance as well as employs a semantic-animation mapping to facilitate LLM-driven creative intent translation. Besides, the system supports synchronized text-animation previews and parametric adjustments via unified controls to improve editing workflow. A user study evaluates the system, highlighting its ability to help non-professional users complete animation workflows while validating the pipeline. The findings encourage further exploration of integrating LLMs into a comprehensive video creation workflow.
@article{arxiv.2506.10762,
title = {Integrating Large Language Models into Text Animation: An Intelligent Editing System with Inline and Chat Interaction},
author = {Bao Zhang and Zihan Li and Zhenglei Liu and Huanchen Wang and Yuxin Ma},
journal= {arXiv preprint arXiv:2506.10762},
year = {2025}
}