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Many social media users prefer consuming content in the form of videos ratherthan text. However, in order for content creators to produce videos with a highclick-through rate, much editing is needed to match the footage to the music.This posts additional challenges for more amateur video makers. Therefore, wepropose a novel attention-based model VMT (Video-Music Transformer) thatautomatically generates piano scores from video frames. Using music generatedfrom models also prevent potential copyright infringements that often come withusing existing music. To the best of our knowledge, there is no work besidesthe proposed VMT that aims to compose music for video. Additionally, therelacks a dataset with aligned video and symbolic music. We release a new datasetcomposed of over 7 hours of piano scores with fine alignment between pop musicvideos and MIDI files. We conduct experiments with human evaluation on VMT,SeqSeq model (our baseline), and the original piano version soundtrack. VMTachieves consistent improvements over the baseline on music smoothness andvideo 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 matchingsoundtrack for videos. We have released our code athttps://github.com/linchintung/VMT

InverseMV: Composing Piano Scores with a Convolutional Video-Music Transformer