LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editing
(2402.10294)Abstract
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 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.
Overview
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LAVE introduces a system leveraging LLMs to simplify video editing, making it more accessible for novices.
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The system features a Language-Augmented Video Gallery, a Video Editing Timeline, and a conversational Video Editing Agent to assist users.
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Utilizes visual-language models (VLMs) to provide language descriptions of video content, enhancing the editing process with LLM-powered computational pipelines.
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A user study highlighted positive experiences, underscoring the flexibility and creativity facilitated by LAVE, and pointed to future directions in natural language integration for creative tasks.
LAVE: Leveraging LLMs for Enhanced Video Editing Experiences
Introduction to LAVE
Video editing is a dynamic and essential aspect of modern digital communication, yet it presents notable challenges, particularly for novices. The complexity and skill required to navigate advanced editing software can deter potential creators. Addressing this issue, the integration of LLMs into the video editing workflow introduces a transformative approach, making it more accessible and reducing the barriers for beginners. This is encapsulated in the development of LAVE, a system that embodies the potential of LLM-powered agent assistance and language augmentation to simplify and enhance the video editing process.
System Design and Key Features
LAVE's architecture is designed around the goal of harnessing natural language to streamline video editing. It achieves this through several innovative components:
- Language-Augmented Video Gallery: Automatically generated textual narrations provide semantic titles and summaries for the user's footage, facilitating an intuitive grasp of the video content without the need to manually scrub through clips.
- Video Editing Timeline: Offers both manual editing capabilities and LLM-based planning and execution features, catering to diverse user preferences and maintaining the creative intent of the video editor.
- Video Editing Agent: A conversational agent assists users throughout the editing process. Capable of understanding free-form language commands, the agent efficiently plans and executes a range of editing actions based on user objectives.
Implementation Insights
At the core of LAVE is its LLM-powered computational pipeline, which automates tasks such as brainstorming, semantic-based video retrieval, and clip sequencing. The use of visual-language models (VLMs) is particularly noteworthy, enabling the system to generate comprehensive language descriptions of video content. This lays a linguistic foundation for the LLM, facilitating the understanding of video material and significantly enhancing the editing process.
Evaluation and User Experiences
A user study with participants of varying editing expertise revealed positive feedback regarding LAVE's effectiveness. Users appreciated the flexibility offered by the dual interaction modalities—agent assistance and direct manipulation—highlighting LAVE’s role in fostering creativity and the sense of co-creation with AI. The study also underlined the importance of providing adaptive agent support, recognizing the diversity in user needs and preferences across different editing tasks.
Research Implications and Future Directions
LAVE's development and user study provide several insights for the future of agent-assisted content editing. Key among these is the potential for natural language to significantly lower the barriers to complex creative tasks, such as video editing. Furthermore, the adaptive nature of agent support and the importance of preserving user agency in the creative process emerge as critical design considerations. Looking forward, the field stands on the cusp of major advancements, with the integration of more sophisticated LLMs and VLMs presenting promising opportunities to further streamline and enhance the video editing experience.
Conclusion
LAVE’s exploration into LLM-powered video editing represents a significant step toward democratizing video creation. By aligning the linguistic capabilities of LLMs with the visual narrative of video content, LAVE not only simplifies the editing process but also opens up new avenues for creative expression. As the technology evolves, the integration of AI in creative processes promises to unlock unprecedented opportunities for content creators, making the act of creation more accessible and enjoyable for all.
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