VidMuse
| Property | Value |
|---|---|
| Author | HKUSTAudio |
| Paper | arXiv:2406.04321 |
| Framework | Video-to-Music Generation |
| Status | Accepted to CVPR 2025 |
What is VidMuse?
VidMuse is an innovative framework designed to bridge the gap between visual and audio content by generating high-fidelity music that aligns perfectly with video content. Using advanced Long-Short-Term modeling techniques, it creates musical compositions that complement video sequences, making it a valuable tool for content creators and multimedia professionals.
Implementation Details
The framework utilizes a sophisticated architecture that processes both local and global video features. It operates at a 32kHz sampling rate and employs a dual-tensor approach for video processing, combining local temporal details with broader contextual information.
- Python-based implementation with Conda environment support
- Integrated with ffmpeg for video processing
- Supports both CPU and GPU processing
- Uses advanced tensor processing for video feature extraction
Core Capabilities
- High-fidelity music generation aligned with video content
- Long-Short-Term temporal modeling for coherent musical sequences
- Flexible video input processing
- Automated audio-video merging capabilities
- Support for various video formats and durations
Frequently Asked Questions
Q: What makes this model unique?
VidMuse stands out for its Long-Short-Term modeling approach, which allows it to generate music that maintains both local synchronization with video events and global musical coherence. Its acceptance to CVPR 2025 validates its innovative contribution to the field.
Q: What are the recommended use cases?
The model is ideal for automatic background music generation for videos, content creation, multimedia production, and research in audio-visual alignment. It's particularly useful for creators who need custom music that matches their video content.
