Multi-agent collaboration for coherent long-video music synthesis

Area of Science:

• Artificial Intelligence
• Computer Science
• Digital Media

Background:

• Video content is prevalent in digital media.
• Background music enhances emotional immersion and narrative.
• Generating music for long videos with diverse scenes and emotions is challenging.

Purpose of the Study:

• To develop a novel framework for generating semantically consistent, temporally aligned, and stylistically coherent music for long videos.
• To address the challenges of automatic music generation in complex video content.

Main Methods:

• A hierarchical multi-agent framework was developed.
• The framework integrates storyboard-based semantic structuring.
• A dual-path feature fusion mechanism and closed-loop self-correction strategy were employed.

Main Results:

• The proposed method outperforms state-of-the-art approaches.
• Demonstrated superior audio quality, semantic consistency, and temporal alignment.
• Sets a new standard for automatic music generation in long videos.

Conclusions:

• The novel framework effectively generates high-quality music for long videos.
• Achieved significant improvements in semantic and temporal aspects of music generation.
• Provides a robust solution for automatic background music creation.