title: LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video
    Editing
summary: 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.
paper_authors._id: 65d2d78ff5a15aa421fd7339
paper_authors.name: Bryan Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-19T15:46:50.083Z
paper_authors._id: 6215a70a6f827861d125fe2e
paper_authors.avatarUrl: /avatars/c84b243e02ca2b71af079c8220c5fe44.svg
paper_authors.fullname: Bryan Wang
paper_authors.type: user
paper_authors.user: bw0514
paper_authors._id: 65d2d78ff5a15aa421fd733a
paper_authors.name: Yuliang Li
paper_authors._id: 65d2d78ff5a15aa421fd733b
paper_authors.name: Zhaoyang Lv
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-02-19T04:22:41.112Z
paper_authors._id: 64a5b3ba84f954be0b716ac8
paper_authors.avatarUrl: /avatars/f7a1df6229d3c127c609b1f1d5fa9a90.svg
paper_authors.fullname: Zhaoyang Lv
paper_authors.type: user
paper_authors.user: zhaoyang-lv-meta
paper_authors._id: 65d2d78ff5a15aa421fd733c
paper_authors.name: Haijun Xia
paper_authors._id: 65d2d78ff5a15aa421fd733d
paper_authors.name: Yan Xu
paper_authors._id: 65d2d78ff5a15aa421fd733e
paper_authors.name: Raj Sodhi
paper_title: LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video
    Editing
paper_summary: 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.