5 research outputs found
3D terrain generation using neural networks
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.Com o aumento do poder de computação, juntamente com os avanços neste campo na forma de GANs
e cGANs, as Redes Neurais tornaram-se numa proposta atrativa para a geração de conteúdos. Graças
a estes avanços, abriram-se oportunidades para os algoritmos de Geração de Conteúdos
Procedimentais(PCG) explorarem o poder generativo das Redes Neurais para a criação de ferramentas
que permitam aos programadores remover parte da carga criativa e de desenvolvimento imposta em
toda a indústria dos jogos, seja por parte dos investidores que procuram um retorno do seu
investimento ou por parte dos consumidores que querem mais e melhor conteúdo, o mais rápido
possível. Esta dissertação pretende desenvolver uma ferramenta de iniciativa mista PCG, alavancando
cGANs, para criar terrenos 3D cocriados, permitindo aos utilizadores influenciarem diretamente o
conteúdo gerado sem necessidade de terem formação formal sobre a criação de terrenos 3D ou
interações complexas com a ferramenta para influenciar a produção generativa, opondo-se assim a
algoritmos generativos comummente utilizados, que apenas permitem a geração de conteúdo
aleatório ou que são desnecessariamente complexos. Um conjunto de testes feitos a 113 pessoas
online e a 30 pessoas presencialmente, revelaram que é de facto possível desenvolver uma ferramenta
que permita aos utilizadores, de qualquer nível de conhecimento sobre criação de terrenos, e com
uma formação mínima na ferramenta, criar um terreno 3D mais realista do que os terrenos gerados a
partir da solução de estado da arte, como o Perlin Noise, e de uma forma fácil
Urban space simulation based on wave function collapse and convolutional neural network
In this paper, we propose a pipeline of urban space synthesis which leverages Wave Function Collapse (WFC) and Convolutional Neural Networks (CNNs) to train the computer how to design urban space. Firstly, we establish an urban design database. Then, the urban road networks, urban block spatial forms and urban building function layouts are generated by WFC and CNNs and evaluated by designer afterwards. Finally, the 3D models are generated. We demonstrate the feasibility of our pipeline through the case study of the North Extension of Central Green Axis in Wenzhou. This pipeline improves the efficiency of urban design and provides new ways of thinking for architecture and urban design
10 years of the PCG workshop : past and future trends
As of 2020, the international workshop on Procedural Content Generation enters its second decade. The annual workshop, hosted by
the international conference on the Foundations of Digital Games,
has collected a corpus of 95 papers published in its first 10 years.
This paper provides an overview of the workshop’s activities and
surveys the prevalent research topics emerging over the years.peer-reviewe
Addressing the fundamental tension of PCGML with discriminative learning
Procedural content generation via machine learning (PCGML) is typically
framed as the task of fitting a generative model to full-scale examples of a
desired content distribution. This approach presents a fundamental tension: the
more design effort expended to produce detailed training examples for shaping a
generator, the lower the return on investment from applying PCGML in the first
place. In response, we propose the use of discriminative models (which capture
the validity of a design rather the distribution of the content) trained on
positive and negative examples. Through a modest modification of
WaveFunctionCollapse, a commercially-adopted PCG approach that we characterize
as using elementary machine learning, we demonstrate a new mode of control for
learning-based generators. We demonstrate how an artist might craft a focused
set of additional positive and negative examples by critique of the generator's
previous outputs. This interaction mode bridges PCGML with mixed-initiative
design assistance tools by working with a machine to define a space of valid
designs rather than just one new design