34,627 research outputs found
Playing for Data: Ground Truth from Computer Games
Recent progress in computer vision has been driven by high-capacity models
trained on large datasets. Unfortunately, creating large datasets with
pixel-level labels has been extremely costly due to the amount of human effort
required. In this paper, we present an approach to rapidly creating
pixel-accurate semantic label maps for images extracted from modern computer
games. Although the source code and the internal operation of commercial games
are inaccessible, we show that associations between image patches can be
reconstructed from the communication between the game and the graphics
hardware. This enables rapid propagation of semantic labels within and across
images synthesized by the game, with no access to the source code or the
content. We validate the presented approach by producing dense pixel-level
semantic annotations for 25 thousand images synthesized by a photorealistic
open-world computer game. Experiments on semantic segmentation datasets show
that using the acquired data to supplement real-world images significantly
increases accuracy and that the acquired data enables reducing the amount of
hand-labeled real-world data: models trained with game data and just 1/3 of the
CamVid training set outperform models trained on the complete CamVid training
set.Comment: Accepted to the 14th European Conference on Computer Vision (ECCV
2016
Agents for educational games and simulations
This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications
Modern Trends in the Automatic Generation of Content for Video Games
Attractive and realistic content has always played a crucial
role in the penetration and popularity of digital games, virtual
environments, and other multimedia applications. Procedural content
generation enables the automatization of production of any type of game
content including not only landscapes and narratives but also game
mechanics and generation of whole games. The article offers a
comparative analysis of the approaches to automatic generation of
content for video games proposed in last five years. It suggests a new
typology of the use of procedurally generated game content comprising of
categories structured in three groups: content nature, generation process,
and game dependence. Together with two other taxonomies â one of
content type and the other of methods for content generation â this
typology is used for comparing and discussing some specific approaches to
procedural content generation in three promising research directions
based on applying personalization and adaptation, descriptive languages,
and semantic specifications
A holistic approach for semantic-based game generation
The Web contains vast sources of content that could
be reused to reduce the development time and effort to create
games. However, most Web content is unstructured and lacks
meaning for machines to be able to process and infer new
knowledge. The Web of Data is a term used to describe a trend
for publishing and interlinking previously disconnected datasets
on the Web in order to make them more valuable and useful as
a whole. In this paper, we describe an innovative approach that
exploits Semantic Web technologies to automatically generate
games by reusing Web content. Existing work on automatic game
content generation through algorithmic means focuses primarily
on a set of parameters within constrained game design spaces
such as terrains or game levels, but does not harness the potential
of already existing content on the Web for game generation. We
instead propose a holistic and more generally-applicable game
generation solution that would identify suitable Web information
sources and enrich game content with semantic meta-structures.The research work disclosed in this publication is partially
funded by the REACH HIGH Scholars Programme â Post-
Doctoral Grants. The grant is part-financed by the European
Union, Operational Programme II â Cohesion Policy 2014-
2020 Investing in human capital to create more opportunities
and promote the wellbeing of society â European Social
Fund.peer-reviewe
- âŠ