VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling
<|reference_start|>VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling: In this work, we systematically study music generation conditioned solely on the video. First, we present a large-scale dataset comprising 190K video-music pairs, including various genres such as movie trailers, advertisements, and documentaries. Furthermore, we propose VidMuse, a simple framework for generating music aligned with video inputs. VidMuse stands out by producing high-fidelity music that is both acoustically and semantically aligned with the video. By incorporating local and global visual cues, VidMuse enables the creation of musically coherent audio tracks that consistently match the video content through Long-Short-Term modeling. Through extensive experiments, VidMuse outperforms existing models in terms of audio quality, diversity, and audio-visual alignment. The code and datasets will be available at https://github.com/ZeyueT/VidMuse/.<|reference_end|>
@article{tian2024vidmuse:,
    title={VidMuse: A Simple Video-to-Music Generation Framework with
  Long-Short-Term Modeling},
    author={Zeyue Tian, Zhaoyang Liu, Ruibin Yuan, Jiahao Pan, Qifeng Liu, Xu Tan,
  Qifeng Chen, Wei Xue, Yike Guo},
    journal={arXiv preprint arXiv:2406.04321},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04321},
    primaryClass={cs.CV cs.LG cs.MM cs.SD}
}