Keywords

Language Models, Narrative Transformation, Generative AI, Scene Generation, Multimedia Content, Diffusion Models, Text-to-Speech, Story Generalization

Abstract

When a production company commits to creating a film based on a novel, it is essential that their team is equipped to manage the extensive responsibilities required to authentically translate the book to the big screen. This study aims to explore and address these challenges by utilizing contemporary Generative Artificial Intelligence technologies, including Large Language Models, Text-To-Speech, and Text-To-Image models. While recent advancements have focused on enhancing these models, there is a gap in research on their practical application and effectiveness in real-world scenarios. This research will detail the steps necessary to deconstruct a novel’s narrative and produce the final cinematic product. Additionally, it will propose novel methods to mitigate errors such as hallucinations generated by Language Models and image models, enhancing the fidelity and quality of the adaptations. iii

Completion Date

2024

Semester

Summer

Committee Chair

Mantzaris, Alexander

Degree

Master of Science (M.S.)

College

College of Sciences

Department

Statistics & Data Science

Degree Program

Statistics and Data Science

Format

application/pdf

Identifier

DP0028528

URL

https://purls.library.ucf.edu/go/DP0028528

Language

English

Release Date

8-15-2024

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Campus Location

Orlando (Main) Campus

Accessibility Status

Meets minimum standards for ETDs/HUTs

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