Trailer Reimagined: An Innovative, Llm-DRiven, Expressive Automated Movie Summary framework (TRAILDREAMS)

TL;DR

This paper presents TRAILDREAMS, an innovative framework leveraging large language models to automate and enhance the creation of movie trailers, focusing on visual, dialogue, and audio elements to produce engaging content.

Contribution

The paper introduces TRAILDREAMS, a novel LLM-driven framework that automates key aspects of trailer creation, surpassing existing methods in viewer ratings.

Findings

TRAILDREAMS outperforms current state-of-the-art trailer generation methods in viewer ratings.

It effectively selects key visual and dialogue elements for trailers.

There remains a quality gap compared to human-crafted trailers.

Abstract

This paper introduces TRAILDREAMS, a framework that uses a large language model (LLM) to automate the production of movie trailers. The purpose of LLM is to select key visual sequences and impactful dialogues, and to help TRAILDREAMS to generate audio elements such as music and voiceovers. The goal is to produce engaging and visually appealing trailers efficiently. In comparative evaluations, TRAILDREAMS surpasses current state-of-the-art trailer generation methods in viewer ratings. However, it still falls short when compared to real, human-crafted trailers. While TRAILDREAMS demonstrates significant promise and marks an advancement in automated creative processes, further improvements are necessary to bridge the quality gap with traditional trailers.