Multi-Agent Content Generation for Controllable AI Video Creation
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Solution Overview
Problem
Current video generation using AI-generated content (AIGC) technologies face challenges in controllability, duration, quality, and cost, requiring significant manual participation and 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 AI-generated content technology is used for video generation, then content creation capability is improved, but controllability deteriorates
Solution Approach 1:
The patent segments the video generation process into multiple independent controllable modules including text-to-video generation, image-to-video generation, and video editing functions. Each module can be independently controlled and adjusted, allowing users to maintain oversight and control over different aspects of content generation while still leveraging AI capabilities.
2Manufacturing precision
If manual participation is increased to improve quality, then content quality is improved, but production efficiency deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where generation results are automatically evaluated and used to adjust subsequent generation parameters. The system provides feedback loops that allow iterative optimization of content quality without requiring continuous manual intervention, maintaining high quality while preserving automation efficiency.
3Device complexity
If complex tasks are handled by single AI model, then task completion is simplified, but task execution quality deteriorates
Solution Approach 1:
The patent merges multiple specialized AI models and tools into a unified video generation system that can handle complex tasks. By combining text-to-video models, image-to-video models, and video editing tools within a single integrated platform, the system maintains simple task execution interfaces while achieving high execution quality through coordinated multi-model processing.
Data Source
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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 (S101); 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 (S102).