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Source note2 min read

VideoAgent: All-in-One Framework for Video Understanding and Editing

Source

arXiv

arxiv.org/abs/2606.23327v1

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Plain-Language Brief

Researchers have developed a new framework called VideoAgent to automate video editing tasks. The system can handle diverse editing operations and understand long videos, addressing limitations in existing automated systems. The framework includes two key innovations: automated shot creation with shot planning agents and cross-modal retrieval for aligned visual content, as well as a multi-agent orchestration framework integrating over thirty specialized editing agents.

What the Document Says

  • The researchers proposed VideoAgent, an all-in-one framework for video understanding and editing.
  • VideoAgent addresses the challenges of handling diverse video comprehension and editing operations and lack of long-video understanding for coherent narrative creation.
  • The framework includes automated video shot creation with shot planning agents and cross-modal retrieval for aligned visual content.
  • A multi-agent orchestration framework integrating over thirty specialized editing agents is also part of VideoAgent.
  • Intent parsing filters relevant tools, while textual-gradient graph optimization assembles complex editing pipelines in the multi-agent orchestration framework.
  • Extensive experiments on the newly-proposed VideoEdit benchmark and public datasets show VideoAgent's superiority over existing multimodal LLMs and agentic systems.
  • VideoAgent achieves 87-95% orchestration success rates and reduces API costs by 60%.
  • Human evaluation across six video categories shows that VideoAgent produces professional-quality content approaching human-level performance, with ratings only 4% below human-created videos.

Key Concepts Mentioned

  • VideoAgent
  • video editing
  • automated video shot creation
  • cross-modal retrieval
  • multi-agent orchestration
  • intent parsing
  • textual-gradient graph optimization
  • orchestration success rates
  • API costs
  • human evaluation

Claims Made by the Document

  • VideoAgent addresses limitations in existing automated systems.
  • VideoAgent achieves high orchestration success rates and reduces API costs.
  • VideoAgent produces professional-quality content approaching human-level performance.

Evidence Used

  • The text mentions that VideoAgent is proposed to address the limitations of existing systems, and it achieves high orchestration success rates and reduced API costs as demonstrated by experiments.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Video editing has become essential in digital media creation, yet existing automated systems are restricted to short segment processing and domain-specific tasks. They face two critical limitations: i) inability to handle diverse video comprehension and editing operations, and ii) lack of long-video understanding for coherent narrative creation. We propose VideoAgent, an all-in-one agentic framework addressing these challenges through two key innovations. First, we develop automated video shot creation with shot planning agents for coherent narratives and cross-modal retrieval for aligned visual content. Second, we design a multi-agent orchestration framework integrating over thirty specialized editing agents. Intent parsing filters relevant tools while textual-gradient graph optimization assembles complex editing pipelines. Extensive experiments on our newly-proposed VideoEdit benchmark and public datasets demonstrate VideoAgent's superiority over existing multimodal LLMs and agentic systems. VideoAgent achieves 87-95% orchestration success rates while reducing API costs by 60%. Human evaluation across six video categories shows VideoAgent produces professional-quality content approaching human-level performance, with ratings only 4% below human-created videos. We release our code at https://github.com/HKUDS/VideoAgent.