Prompt-Driven Agentic Video Editing System: Autonomous Comprehension of Long-Form, Story-Driven Media
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914060601131008 |
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| author | Ding, Zihan Wang, Xinyi Chen, Junlong Kristensson, Per Ola Shen, Junxiao |
| author_facet | Ding, Zihan Wang, Xinyi Chen, Junlong Kristensson, Per Ola Shen, Junxiao |
| contents | Creators struggle to edit long-form, narrative-rich videos not because of UI complexity, but due to the cognitive demands of searching, storyboarding, and sequencing hours of footage. Existing transcript- or embedding-based methods fall short for creative workflows, as models struggle to track characters, infer motivations, and connect dispersed events. We present a prompt-driven, modular editing system that helps creators restructure multi-hour content through free-form prompts rather than timelines. At its core is a semantic indexing pipeline that builds a global narrative via temporal segmentation, guided memory compression, and cross-granularity fusion, producing interpretable traces of plot, dialogue, emotion, and context. Users receive cinematic edits while optionally refining transparent intermediate outputs. Evaluated on 400+ videos with expert ratings, QA, and preference studies, our system scales prompt-driven editing, preserves narrative coherence, and balances automation with creator control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16811 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prompt-Driven Agentic Video Editing System: Autonomous Comprehension of Long-Form, Story-Driven Media Ding, Zihan Wang, Xinyi Chen, Junlong Kristensson, Per Ola Shen, Junxiao Artificial Intelligence Human-Computer Interaction Creators struggle to edit long-form, narrative-rich videos not because of UI complexity, but due to the cognitive demands of searching, storyboarding, and sequencing hours of footage. Existing transcript- or embedding-based methods fall short for creative workflows, as models struggle to track characters, infer motivations, and connect dispersed events. We present a prompt-driven, modular editing system that helps creators restructure multi-hour content through free-form prompts rather than timelines. At its core is a semantic indexing pipeline that builds a global narrative via temporal segmentation, guided memory compression, and cross-granularity fusion, producing interpretable traces of plot, dialogue, emotion, and context. Users receive cinematic edits while optionally refining transparent intermediate outputs. Evaluated on 400+ videos with expert ratings, QA, and preference studies, our system scales prompt-driven editing, preserves narrative coherence, and balances automation with creator control. |
| title | Prompt-Driven Agentic Video Editing System: Autonomous Comprehension of Long-Form, Story-Driven Media |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.16811 |