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.

通过将大型语言模型（LLMs）与视频编辑流程相结合，我们提出了一种名为 LAVE 的新系统，使用 LLMs 提供代理辅助和语言增强的编辑功能，以减少初学者的编辑障碍，并通过用户研究证明了 LAVE 的有效性和对用户创造力和共创感的影响。