In this paper, we have introduced the SymMV dataset,
which contains 1140 videos and corresponding background
music with rich annotations. Based on SymMV, we devel-
oped a benchmark model V-MusProd. It decouples music
into chords, melody, and accompaniment, then utilizes video-
music relations of semantic, color, and motion features to
guide the generation process. We also introduced the VMCP
metric based on video-music CLIP to evaluate video-music
correspondence. With VMCP and subjective evaluation, we
prove that V-MusProd outperforms baseline model CMT in
correspondence both qualitatively and quantitatively.

Acknowledgement This work was supported in part by
the National Key R&D Program of China under Grant
2022ZD0115502, the National Natural Science Foundation
of China under Grant 62122010, and the CCF-DiDi GAIA
Collaborative Research Funds for Young Scholars.
VideoBackgroundMusicGeneration