AI Agent Framework for Controllable Video Content Generation
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Solution Overview
Problem
Current video generation using AI-generated content (AIGC) technologies require significant manual participation due to issues with controllability, duration, quality, and cost, limiting production efficiency.
Innovation Solution
A content generation method utilizing an agent framework that leverages intelligent agents to split content generation tasks into multiple sub-tasks, utilizing various tools and models to iteratively generate high-quality content by exchanging task execution requirements and results between intelligent agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual participation is used in video generation, then quality and controllability can be maintained, but production efficiency is limited
Solution Approach 1:
The patent divides the video generation task into multiple sub-tasks that are distributed across different intelligent agents. Each agent specializes in specific aspects such as script generation, scene planning, video synthesis, and quality evaluation. This segmentation enables automated processing of complex video generation workflows while maintaining quality through specialized agent functions, thereby resolving the contradiction between automation and quality control.
2Productivity
If complex content generation processes are automated, then production efficiency improves, but system complexity increases
Solution Approach 1:
The patent creates a universal agent framework where multiple intelligent agents can be deployed to handle different aspects of content generation. The framework provides standardized interfaces and protocols that allow agents to work together seamlessly. This multi-functional architecture enables the system to handle diverse content generation tasks through a unified automated platform, improving productivity while managing complexity through standardization.
3Manufacturing precision
If iterative task execution is implemented, then content quality improves, but execution time increases
Solution Approach 1:
The patent implements feedback mechanisms where intelligent agents evaluate task execution results and use this information to refine subsequent iterations. Quality evaluation agents assess generated content and provide feedback to generation agents, enabling continuous improvement without requiring excessive iterations. This feedback-driven approach improves content quality while controlling execution time by directing refinement efforts toward specific areas that need improvement.
Data Source
AI summary
Provided is a content generation method and apparatus based on artificial intelligence, a device and a storage medium, relating to the fields of computer vision, deep learning, large model, and intelligent agent. The content generation method includes: sending, by a first intelligent agent, a task execution requirement to a second intelligent agent according to task guidance information, wherein the task guidance information comprises guidance information for generating content, and the task execution requirement comprises a target task that needs to be executed by the second intelligent agent to generate content; and receiving, by the first intelligent agent, a task execution result from the second intelligent agent, wherein the task execution result comprises an execution result generated after the second intelligent agent executes the target task.