3D terrain generation using neural networks

• Autor(a) principal: Almeida, Rodrigo de Matos Pires Tavares de
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/22222
• topic: Neural networks; Procedural content generation; PCG; GAN; cGAN; 3D terrain; Perlin noise; Redes neuronais; Geração procedimental de conteúdos; Terrenos 3D

With the increase in computation power, coupled with the advancements in the field in the form of GANs and cGANs, Neural Networks have become an attractive proposition for content generation. This opened opportunities for Procedural Content Generation algorithms (PCG) to tap Neural Networks generative power to create tools that allow developers to remove part of creative and developmental burden imposed throughout the gaming industry, be it from investors looking for a return on their investment and from consumers that want more and better content, fast. This dissertation sets out to develop a PCG mixed-initiative tool, leveraging cGANs, to create authored 3D terrains, allowing users to directly influence the resulting generated content without the need for formal training on terrain generation or complex interactions with the tool to influence the generative output, as opposed to state of the art generative algorithms that only allow for random content generation or are needlessly complex. Testing done to 113 people online, as well as in-person testing done to 30 people, revealed that it is indeed possible to develop a tool that allows users from any level of terrain creation knowledge, and minimal tool training, to easily create a 3D terrain that is more realistic looking than those generated by state-of-the-art solutions such as Perlin Noise.