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2020 | OriginalPaper | Chapter

Procedural Content Generation via Machine Learning in 2D Indoor Scene

Authors : Bruno Ježek, Adam Ouhrabka, Antonin Slabý

Published in: Augmented Reality, Virtual Reality, and Computer Graphics

Publisher: Springer International Publishing

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Abstract

The article proposes a method of combining multiple deep forward neural networks to generate a distribution of objects in a 2D scene. The main concepts of machine learning, neural networks and procedural content generation concerning this intention are presented here. Additionally, these concepts are put into the context of computer graphics and used in a practical example of generating an indoor 2D scene. A method of vectorization of input datasets for training forward neural networks is proposed. Scene generation is based on the consequent placement of objects of different classes into the free space defining a room of a certain shape. Several evaluate methods have been proposed for testing the correctness of generation.

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Title: Procedural Content Generation via Machine Learning in 2D Indoor Scene
Authors: Bruno Ježek
Adam Ouhrabka
Antonin Slabý
Publisher: Springer International Publishing
Book: Augmented Reality, Virtual Reality, and Computer Graphics
Print ISBN: 978-3-030-58464-1

Electronic ISBN: 978-3-030-58465-8

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-58465-8_3

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