Lastly, another bottleneck of video background music
generation lies in the inadequate objective metrics. Previous
methods [9, 56, 45] use metrics for unconditional music gen-
eration or subjective evaluation to examine the video-music
correspondence. Common metrics for unconditional music
generation can only assess the quality and diversity of the
generated samples but ignores the alignment between paired
music and videos. Besides, conducting human listening
tests for subjective evaluation is cumbersome and sometimes
biased. Recent text-to-image generation models [34, 39]
leverage the powerful pretrained vision-language CLIP [36]
model to compute the similarity between input prompts and
VideoBackgroundMusicGeneration