Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art

• URL: http://arxiv.org/abs/2311.10129v1
• Date: Thu, 16 Nov 2023 18:36:16 GMT
• Title: Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art
• Authors: Kaisei Fukaya, Damon Daylamani-Zad, Harry Agius
• Abstract summary: Procedural content generation can be applied to a wide variety of tasks in games, from narratives, levels and sounds, to trees and weapons. This paper explores state-of-the-art approaches to graphical asset generation, examining research from a wide range of applications, inside and outside of games.
• Score: 1.534667887016089
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Procedural content generation (PCG) can be applied to a wide variety of tasks in games, from narratives, levels and sounds, to trees and weapons. A large amount of game content is comprised of graphical assets, such as clouds, buildings or vegetation, that do not require gameplay function considerations. There is also a breadth of literature examining the procedural generation of such elements for purposes outside of games. The body of research, focused on specific methods for generating specific assets, provides a narrow view of the available possibilities. Hence, it is difficult to have a clear picture of all approaches and possibilities, with no guide for interested parties to discover possible methods and approaches for their needs, and no facility to guide them through each technique or approach to map out the process of using them. Therefore, a systematic literature review has been conducted, yielding 200 accepted papers. This paper explores state-of-the-art approaches to graphical asset generation, examining research from a wide range of applications, inside and outside of games. Informed by the literature, a conceptual framework has been derived to address the aforementioned gaps.

Related papers

• Pixels to Prose: Understanding the art of Image Captioning [1.9635669040319872]
  Image captioning enables machines to interpret visual content and generate descriptive text. The review traces the evolution of image captioning models to the latest cutting-edge solutions. The paper also delves into the application of image captioning in the medical domain.
  arXiv  Detail & Related papers  (2024-08-28T11:21:23Z)
• A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenes [80.20670062509723]
  3D dense captioning is an emerging vision-language bridging task that aims to generate detailed descriptions for 3D scenes. It presents significant potential and challenges due to its closer representation of the real world compared to 2D visual captioning. Despite the popularity and success of existing methods, there is a lack of comprehensive surveys summarizing the advancements in this field.
  arXiv  Detail & Related papers  (2024-03-12T10:04:08Z)
• Panel Transitions for Genre Analysis in Visual Narratives [1.320904960556043]
  We present a novel approach to do a multi-modal analysis of genre based on comics and manga-style visual narratives. We highlight some of the limitations and challenges of our existing computational approaches in modeling subjective labels.
  arXiv  Detail & Related papers  (2023-12-14T08:05:09Z)
• Vision-Language Pre-training: Basics, Recent Advances, and Future Trends [158.34830433299268]
  Vision-language pre-training methods for multimodal intelligence have been developed in the last few years. For each category, we present a comprehensive review of state-of-the-art methods, and discuss the progress that has been made and challenges still being faced. In addition, we discuss advanced topics being actively explored in the research community, such as big foundation models, unified modeling, in-context few-shot learning, knowledge, robustness, and computer vision in the wild, to name a few.
  arXiv  Detail & Related papers  (2022-10-17T17:11:36Z)
• Mapping Research Trajectories [0.0]
  We propose a principled approach for emphmapping research trajectories, which is applicable to all kinds of scientific entities. Our visualizations depict the research topics of entities over time in a straightforward interpr. manner. In a practical demonstrator application, we exemplify the proposed approach on a publication corpus from machine learning.
  arXiv  Detail & Related papers  (2022-04-25T13:32:39Z)
• Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives [81.88384269259706]
  We introduce Automatic Image Content Extraction framework for machine learning-based search and analysis of large image archives. The proposed framework can be applied in several domains in humanities and social sciences.
  arXiv  Detail & Related papers  (2022-04-05T12:19:24Z)
• CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories [65.35714948506032]
  The Curiosity-Conditioned Proximal Trajectories (CCPT) method combines curiosity and imitation learning to train agents to explore. We show how CCPT can explore complex environments, discover gameplay issues and design oversights in the process, and recognize and highlight them directly to game designers.
  arXiv  Detail & Related papers  (2022-02-21T09:08:33Z)
• Deep learning for video game genre classification [2.66512000865131]
  This paper proposes a multi-modal deep learning framework to solve this problem. We compiles a large dataset consisting of 50,000 video games from 21 genres made of cover images, description text, and title text and the genre information. Results show that the multi-modal framework outperforms the current state-of-the-art image-based or text-based models.
  arXiv  Detail & Related papers  (2020-11-21T22:31:43Z)
• Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games [64.11746320061965]
  We study reinforcement learning for text-based games, which are interactive simulations in the context of natural language. We aim to conduct explicit reasoning with knowledge graphs for decision making, so that the actions of an agent are generated and supported by an interpretable inference procedure. We extensively evaluate our method on a number of man-made benchmark games, and the experimental results demonstrate that our method performs better than existing text-based agents.
  arXiv  Detail & Related papers  (2020-10-22T12:40:22Z)
• Image Segmentation Using Deep Learning: A Survey [58.37211170954998]
  Image segmentation is a key topic in image processing and computer vision. There has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models.
  arXiv  Detail & Related papers  (2020-01-15T21:37:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.