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Related Experiment Video

Updated: Jun 29, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.7K

SPAgent: Adaptive Task Decomposition and Model Selection for General Video Generation and Editing.

Rong-Cheng Tu, Wenhao Sun, Zhao Jin

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 19, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the Semantic Planning Agent (SPAgent), an AI system that automatically orchestrates video generation and editing models. SPAgent enhances versatility and adaptability for complex video tasks, overcoming manual coordination challenges.

    Related Experiment Videos

    Last Updated: Jun 29, 2026

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.7K

    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Machine Learning

    Background:

    • Current video generation and editing models are task-specific, limiting their utility.
    • Manual coordination of these models is complex and requires significant expertise.
    • A need exists for automated systems to manage diverse video generation and editing tasks.

    Purpose of the Study:

    • To propose the Semantic Planning Agent (SPAgent) for automatic coordination of open-source video models.
    • To enable SPAgent to fulfill complex user intents for video generation and editing.
    • To enhance the versatility and adaptability of AI-driven video production.

    Main Methods:

    • Developed a three-step framework: decoupled intent recognition, principle-guided route planning, and capability-based model selection.
    • Curated a comprehensive multi-task generative video dataset for training.
    • Integrated a video quality evaluation module for autonomous model assessment and library expansion.

    Main Results:

    • SPAgent effectively coordinates state-of-the-art open-source models for video generation and editing.
    • Demonstrated superior versatility and adaptability across a wide range of video tasks.
    • Achieved high-quality video output through automated model orchestration.

    Conclusions:

    • SPAgent offers a robust solution for automating complex video generation and editing workflows.
    • The system's autonomous capabilities allow for continuous improvement and adaptation.
    • SPAgent significantly advances the field of AI-powered video content creation.