21,088 research outputs found
Automatic evolution of programs for procedural generation of terrains for video games: accessibility and edge length constraints
Nowadays the video game industry is facing
a big challenge: keep costs under control as games become
bigger and more complex. Creation of game content,
such as character models, maps, levels, textures,
sound effects and so on, represent a big slice of total
game production cost. Hence, the video game industry
is increasingly turning to procedural content generation
to amplify the cost-effectiveness of the efforts of video
game designers. However, procedural methods for automated
content generation are difficult to create and
parametrize. In this work we study a Genetic Programming
based procedural content technique to generate
procedural terrains that do not require parametrization,
thus, allowing to save time and help reducing production
costs. Generated procedural terrains present aesthetic
appeal; however, unlike most techniques involving
aesthetic, our approach does not require a human to
perform the evaluation. Instead, the search is guided by
the weighted sum of two morphological metrics: terrain
accessibility and obstacle edge length. The combination of the two metrics allowed us to find a wide range of fit
terrains that present more scattered obstacles in different
locations, than our previous approach with a single
metric. Procedural terrains produced by this technique are already in use in a real video game
Evolucionando terrenos artificiales con programación genética automatizada de terrenos
Tese de Doutoramento apresentada à Universidad de Extremadura.Nowadays video game industry is facing a big challenge: keep costs under control as games become bigger and more complex. Creation of game content, such as character models, maps, levels, textures, sound effects and so on, represent a big slice of total game production cost. Hence, video game industry is increasingly turning to procedural content generation to amplify the cost-effectiveness of the efforts of video game designers. However, creating and fine tunning procedural methods for automated content generation is a time consuming task.
In this thesis we detail a Genetic Programming based procedural content technique to generate procedural terrains. Those terrains present aesthetic appeal and do not require any parametrization to control its look. Thus, allowing to save time and help reducing production costs. To accomplish these features we devised the Genetic Terrain Programming (GTP) technique.
The first implementation of GTP used an Interactive Evolutionary Computation (IEC) approach, were a user guides the evolutionary process. In spite of the good results achieved this way, this approach was limited by user fatigue (a common trait of IEC systems). To address this issue a second version of GTP was developed where the search is automated, being guided by a direct fitness function. That function is composed by the weighted sum of two morphological metrics: terrain accessibility and obstacle edge length. The combination of the two metrics allowed us remove the human factor form the evolutionary process and to find a wide range of aesthetic and fit terrains. Procedural terrains produced by this technique are already in use in a real video game
Procedural modeling of plant ecosystems maximizing vegetation cover
Vegetation plays a major role in the realistic display of outdoor scenes. However, manual
plant placement can be tedious. For this reason this paper presents a new proposal in the field
of procedural modeling of natural scenes. This method creates plant ecosystems that maximizes the covered space by optimizing an objective function subject to a series of constraints
defined by a system of inequalities. This system includes the constraints of the environment
taking into account characteristics of the terrain and the plant species involved. Once the
inequality system has been defined, a solution will be obtained that tries to maximize the
radius of the projected area of the trees and therefore the extension of the vegetation cover
on the ground. The technique eliminates the trees that do not achieve a minimum growth
radius, simulating the typical competitive process of nature. Results show the good performance and the high visual quality of the ecosystems obtained by the proposed technique.
The use of this kind of optimization techniques could be used to solve other procedural
modeling problems in other fields of application.Funding for open access charge: CRUE-Universitat Jaume
Genetic terrain programming: an aesthetic approach to terrain generation
Comunicação apresentada na conferência Computer Games and Allied Technology, 8, Singapore, 2008.Nowadays there are a wide range of techniques for terrain generation, but are focused on
providing realistic terrains often neglecting the aesthetic appeal. The Genetic Terrain
Programming technique, based on evolutionary design with Genetic Programming, allows
designers to evolve terrains according to their aesthetic feelings or desired features. This
technique evolves TPs (Terrain Programmes) that are capable of generating different terrains, but
consistently with the same features. This paper presents a study about the perseverance of
terrain features of the TPs across different LODs (Levels Of Detail). Results showed it is possible
to use low LODs during the evolutionary phase without compromising results and the terrain
features generated by a TPs are scale invariant
Procedural Generation and Rendering of Realistic, Navigable Forest Environments: An Open-Source Tool
Simulation of forest environments has applications from entertainment and art
creation to commercial and scientific modelling. Due to the unique features and
lighting in forests, a forest-specific simulator is desirable, however many
current forest simulators are proprietary or highly tailored to a particular
application. Here we review several areas of procedural generation and
rendering specific to forest generation, and utilise this to create a
generalised, open-source tool for generating and rendering interactive,
realistic forest scenes. The system uses specialised L-systems to generate
trees which are distributed using an ecosystem simulation algorithm. The
resulting scene is rendered using a deferred rendering pipeline, a Blinn-Phong
lighting model with real-time leaf transparency and post-processing lighting
effects. The result is a system that achieves a balance between high natural
realism and visual appeal, suitable for tasks including training computer
vision algorithms for autonomous robots and visual media generation.Comment: 14 pages, 11 figures. Submitted to Computer Graphics Forum (CGF). The
application and supporting configuration files can be found at
https://github.com/callumnewlands/ForestGenerato
Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance
Sketch-based terrain generation seeks to create realistic landscapes for
virtual environments in various applications such as computer games, animation
and virtual reality. Recently, deep learning based terrain generation has
emerged, notably the ones based on generative adversarial networks (GAN).
However, these methods often struggle to fulfill the requirements of flexible
user control and maintain generative diversity for realistic terrain.
Therefore, we propose a novel diffusion-based method, namely terrain diffusion
network (TDN), which actively incorporates user guidance for enhanced
controllability, taking into account terrain features like rivers, ridges,
basins, and peaks. Instead of adhering to a conventional monolithic denoising
process, which often compromises the fidelity of terrain details or the
alignment with user control, a multi-level denoising scheme is proposed to
generate more realistic terrains by taking into account fine-grained details,
particularly those related to climatic patterns influenced by erosion and
tectonic activities. Specifically, three terrain synthesisers are designed for
structural, intermediate, and fine-grained level denoising purposes, which
allow each synthesiser concentrate on a distinct terrain aspect. Moreover, to
maximise the efficiency of our TDN, we further introduce terrain and sketch
latent spaces for the synthesizers with pre-trained terrain autoencoders.
Comprehensive experiments on a new dataset constructed from NASA Topology
Images clearly demonstrate the effectiveness of our proposed method, achieving
the state-of-the-art performance. Our code and dataset will be publicly
available
Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art
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
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