Towards Automated Movie Trailer Generation
Dawit Mureja Argaw Mattia Soldan Alejandro Pardo Chen Zhao Fabian Caba Heilbron Joon Son Chung Bernard Ghanem
Movie trailers are an essential tool for promoting films and attracting
audiences. However, the process of creating trailers can be time-consuming and
expensive. To streamline this process, we propose an automatic trailer
generation framework that generates plausible trailers from a full movie by
automating shot selection and composition. Our approach draws inspiration from
machine translation techniques and models the movies and trailers as sequences
of shots, thus formulating the trailer generation problem as a
sequence-to-sequence task. We introduce Trailer Generation Transformer (TGT), a
deep-learning framework utilizing an encoder-decoder architecture. TGT movie
encoder is tasked with contextualizing each movie shot representation via
self-attention, while the autoregressive trailer decoder predicts the feature
representation of the next trailer shot, accounting for the relevance of shots'
temporal order in trailers. Our TGT significantly outperforms previous methods
on a comprehensive suite of metrics.