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