Related papers: Film Trailer Generation via Task Decomposition

Film Trailer Generation via Task Decomposition

URL: http://arxiv.org/abs/2111.08774v1
Date: Tue, 16 Nov 2021 20:50:52 GMT
Title: Film Trailer Generation via Task Decomposition
Authors: Pinelopi Papalampidi, Frank Keller, Mirella Lapata
Abstract summary: We model movies as graphs, where nodes are shots and edges denote semantic relations between them. We learn these relations using joint contrastive training which leverages privileged textual information from screenplays. An unsupervised algorithm then traverses the graph and generates trailers that human judges prefer to ones generated by competitive supervised approaches.
Score: 65.16768855902268
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: Movie trailers perform multiple functions: they introduce viewers to the story, convey the mood and artistic style of the film, and encourage audiences to see the movie. These diverse functions make automatic trailer generation a challenging endeavor. We decompose it into two subtasks: narrative structure identification and sentiment prediction. We model movies as graphs, where nodes are shots and edges denote semantic relations between them. We learn these relations using joint contrastive training which leverages privileged textual information (e.g., characters, actions, situations) from screenplays. An unsupervised algorithm then traverses the graph and generates trailers that human judges prefer to ones generated by competitive supervised approaches.

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