LAVE：以LLM為基礎的代理輔助和語言增強視頻編輯

摘要

影片製作越來越受歡迎，然而編輯所需的專業知識和努力常常對初學者構成障礙。本文探討將大型語言模型（LLMs）整合到影片編輯工作流程中，以減少這些障礙。我們的設計理念體現在LAVE中，這是一個提供LLM動力的代理助手和語言增強編輯功能的新穎系統。LAVE自動為用戶的影片生成語言描述，作為啟用LLM處理影片並協助編輯任務的基礎。當用戶提供編輯目標時，代理計劃並執行相關操作以實現目標。此外，LAVE允許用戶通過代理或直接UI操作來編輯影片，提供靈活性並實現對代理操作的手動微調。我們的用戶研究包括從初學者到熟練編輯人員的八名參與者，證明了LAVE的有效性。結果還闡明了用戶對所提出的LLM輔助編輯範式及其對用戶創造力和共同創作感的看法。基於這些發現，我們提出了設計啟示，以指導未來代理輔助內容編輯的發展。

English

Video creation has become increasingly popular, yet the expertise and effort required for editing often pose barriers to beginners. In this paper, we explore the integration of large language models (LLMs) into the video editing workflow to reduce these barriers. Our design vision is embodied in LAVE, a novel system that provides LLM-powered agent assistance and language-augmented editing features. LAVE automatically generates language descriptions for the user's footage, serving as the foundation for enabling the LLM to process videos and assist in editing tasks. When the user provides editing objectives, the agent plans and executes relevant actions to fulfill them. Moreover, LAVE allows users to edit videos through either the agent or direct UI manipulation, providing flexibility and enabling manual refinement of agent actions. Our user study, which included eight participants ranging from novices to proficient editors, demonstrated LAVE's effectiveness. The results also shed light on user perceptions of the proposed LLM-assisted editing paradigm and its impact on users' creativity and sense of co-creation. Based on these findings, we propose design implications to inform the future development of agent-assisted content editing.

LAVE：以LLM為基礎的代理輔助和語言增強視頻編輯

LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editing

摘要

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