Skip to yearly menu bar Skip to main content


Towards Automated Movie Trailer Generation

Dawit Argaw Argaw · Mattia Soldan · Alejandro Pardo · Chen Zhao · Fabian Caba Heilbron · Joon Chung · Bernard Ghanem

Arch 4A-E Poster #257
[ ]
Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


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.

Live content is unavailable. Log in and register to view live content