@inproceedings{aa54c3b77e3b4f1b82bb4a8ddc5d9584,
title = "From images to stories: exploring player-driven narratives in games.",
abstract = "This paper presents a method for generating player-driven narratives from visual inputs by exploring the visual analysis capabilities of multimodal large language models. By employing Bartle{\textquoteright}s taxonomy of player types—Achievers, Explorers, Socializers, and Killers—our method creates stories that are tailored to different player characteristics. We conducted a fourfold experiment using a set of images extracted from a well-known game, generating distinct narratives for each player type that are aligned with the visual elements of the input images and specific player motivations. By adjusting narrative elements to emphasize achievement for Achievers, exploration for Explorers, social connections for Socializers, and competition for Killers, our system produced stories that adhere to established narratology principles while resonating with the characteristics of each player type. This approach can serve as a helping tool for game designers, offering new insights into how players might engage with game worlds through personalized image-driven narratives.",
keywords = "GPT-4o Vision, Image Analysis, Intelligent Agents, Player Types, Story Composition, Text-to-Image",
author = "{Soares de Lima}, Edirlei and MME Neggers and MA Casanova and AL Furtado",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.",
year = "2025",
language = "English",
isbn = "9783031817120",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "228--242",
editor = "Anabela Marto and Roberto Ribeiro and Alexandrino Gon{\c c}alves and Rui Prada and Patr{\'i}cia Gouveia and Espinosa, {Ruth Contreras} and Eduarda Abrantes",
booktitle = "Videogame Sciences and Arts - 14th International Conference, VJ 2024, Proceedings",
}