VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling
In this study, the authors systematically explore the generation of music conditioned solely on video inputs. They introduce a large-scale dataset containing 190,000 video-music pairs, encompassing a variety of genres such as movie trailers, advertisements, and documentaries. The core contribution of this work is VidMuse, a novel framework designed to generate music that aligns seamlessly with video content. VidMuse distinguishes itself by producing high-fidelity music that is acoustically and semantically aligned with the video. It achieves this by leveraging both local and global visual cues, allowing it to create musically coherent audio tracks that consistently match the video content through Long-Short-Term modeling. Extensive experiments demonstrate that VidMuse surpasses existing models in terms of audio quality, diversity, and audio-visual alignment. The code and datasets for this research will be made available at GitHub - ZeyueT/VidMuse.
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