VMAS: Video-to-Music Generation via Semantic Alignment in Web Music
  Videos
Authors: name: Yan-Bo Lin, name: Yu Tian, name: Linjie Yang, name: Gedas Bertasius, name: Heng Wang

We present a framework for learning to generate background music from video
inputs. Unlike existing works that rely on symbolic musical annotations, which
are limited in quantity and diversity, our method leverages large-scale web
videos accompanied by background music. This enables our model to learn to
generate realistic and diverse music. To accomplish this goal, we develop a
generative video-music Transformer with a novel semantic video-music alignment
scheme. Our model uses a joint autoregressive and contrastive learning
objective, which encourages the generation of music aligned with high-level
video content. We also introduce a novel video-beat alignment scheme to match
the generated music beats with the low-level motions in the video. Lastly, to
capture fine-grained visual cues in a video needed for realistic background
music generation, we introduce a new temporal video encoder architecture,
allowing us to efficiently process videos consisting of many densely sampled
frames. We train our framework on our newly curated DISCO-MV dataset,
consisting of 2.2M video-music samples, which is orders of magnitude larger
than any prior datasets used for video music generation. Our method outperforms
existing approaches on the DISCO-MV and MusicCaps datasets according to various
music generation evaluation metrics, including human evaluation. Results are
available at https://genjib.github.io/project_page/VMAs/index.html