Основные понятия
The author presents Video2Music, an innovative framework that generates music to match videos by utilizing a novel Affective Multimodal Transformer model.
Аннотация
Video2Music introduces a unique approach to generating music that aligns with video content. The framework extracts features from both videos and music, employs a Transformer model for generation, and utilizes post-processing for dynamic MIDI output. By addressing the challenge of synchronizing music with visuals, Video2Music offers a promising solution for music-video correspondence.
The work highlights the importance of background music in enhancing viewer experience and storytelling in videos. It discusses the limitations of existing models and datasets for music generation for videos, emphasizing the need for comprehensive datasets like MuVi-Sync.
Through detailed explanations of data extraction processes and model architectures, the paper showcases how Video2Music leverages cutting-edge technology to create expressive and emotionally resonant music-video matches. The proposed Affective Multimodal Transformer model stands out as a pioneering approach in this domain.
Overall, Video2Music represents a significant advancement in the field of generative AI for music-video matching, offering insights into the future potential of multimodal music generation systems.
Статистика
RMSE (Note density) 4.5337
RMSE (Loudness) 0.0882