LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video
  Editing
Bryan Wang Yuliang Li Zhaoyang Lv Haijun Xia Yan Xu Raj Sodhi
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.