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This research presents agentic frameworks for automating video editing tasks ranging from simple scene previews to complex cinematic trailer generation, addressing the intersection of creative planning and technical constraints. By evaluating outputs across varying levels of editorial complexity, the study offers critical insights into how AI agents can effectively manage decision-making processes in multimedia production workflows.
Sources coveringarXiv, The Verge
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Unified Agentic Video Editing Across Levels of Complexity and Creativity

arXiv1 min read

arXiv:2609.12769v1 Announce Type: new Abstract: Editing is a core component of video production, requiring creative planning and decisions under multiple constraints. Here, we report methods for agentic tooling for automated video editing across three tasks varying in editorial goal, complexity and creativity, namely scene previews, video summaries and cinematic trailers. We evaluate the outputs and discuss implications for automation and agency.

Abstract:Editing is a core component of video production, requiring creative planning and decisions under multiple constraints. Here, we report methods for agentic tooling for automated video editing across three tasks varying in editorial goal, complexity and creativity, namely scene previews, video summaries and cinematic trailers. We evaluate the outputs and discuss implications for automation and agency.

Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multimedia (cs.MM)

arXiv:2609.12769 [cs.AI]

(or arXiv:2609.12769v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.12769 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 155 words

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