Integrating Large Language Models into Text Animation: An Intelligent Editing System with Inline and Chat Interaction
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912426926014464 |
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| author | Zhang, Bao Li, Zihan Liu, Zhenglei Wang, Huanchen Ma, Yuxin |
| author_facet | Zhang, Bao Li, Zihan Liu, Zhenglei Wang, Huanchen Ma, Yuxin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10762 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Large Language Models into Text Animation: An Intelligent Editing System with Inline and Chat Interaction Zhang, Bao Li, Zihan Liu, Zhenglei Wang, Huanchen Ma, Yuxin Human-Computer Interaction 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. |
| title | Integrating Large Language Models into Text Animation: An Intelligent Editing System with Inline and Chat Interaction |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.10762 |