  Many social media users prefer consuming content in the form of videos rather
than text. However, in order for content creators to produce videos with a high
click-through rate, much editing is needed to match the footage to the music.
This posts additional challenges for more amateur video makers. Therefore, we
propose a novel attention-based model VMT (Video-Music Transformer) that
automatically generates piano scores from video frames. Using music generated
from models also prevent potential copyright infringements that often come with
using existing music. To the best of our knowledge, there is no work besides
the proposed VMT that aims to compose music for video. Additionally, there
lacks a dataset with aligned video and symbolic music. We release a new dataset
composed of over 7 hours of piano scores with fine alignment between pop music
videos and MIDI files. We conduct experiments with human evaluation on VMT,
SeqSeq model (our baseline), and the original piano version soundtrack. VMT
achieves consistent improvements over the baseline on music smoothness and
video relevance. In particular, with the relevance scores and our case study,
our model has shown the capability of multimodality on frame-level actors'
movement for music generation. Our VMT model, along with the new dataset,
presents a promising research direction toward composing the matching
soundtrack for videos. We have released our code at
https://github.com/linchintung/VMT
