Image-based video game asset generation and evaluation using deep learning: a systematic review of methods and applications

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024

Autores

Ribeiro, R (Autor) Outra; Alexandre Carvalho (Autor) FEUP; Rodrigues, NB (Autor) Outra

Revista

Título: IEEE Transactions on Games

Páginas: 1-10

ISSN: 2475-1502

Editora: IEEE

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-017-CHK

DOI: 10.1109/tg.2024.3487054

Abstract (EN): Creating content for digital video game is an expensive segment of the development process, and many techniques have been explored to automate it. Much of the generated content is graphical, ranging from textures and sprites to typographical elements and user interfaces. Numerous techniques have been explored to automate the generation of these assets, with recent advancements incorporating artificial intelligence methodologies such as deep learning generative models. This study comprehensively surveys the literature from 2016 onwards, focusing on using machine learning to generate image-based assets for video game development, reviewing the deep learning approaches employed, and analyzing the specific challenges found. Specifically, the deep learning approaches employed, the problems addressed within the domain, and the metrics used for evaluating the results. The study demonstrates a knowledge gap in generative methods for some types of video game assets. Additionally, applicability and effectiveness of the most used evaluation metrics in the literature are studied. As future research prospects, with the increase in popularity of generative AI, the adoption of such techniques will be seen in automation processes. © 2018 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica