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Future of Information and Communication Conference

FICC 2020: Advances in Information and Communication pp 400–419Cite as

Conditional Convolutional Generative Adversarial Networks Based Interactive Procedural Game Map Generation

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1129))

Abstract

There is a strong need for a procedural map design system which can generate complex detail game maps but with simple user control. We propose an interactive real-time design system made with Conditional Generative Adversarial Network and Convolutional Neural Network. This system takes user-defined game-play area map as input, and generate a complex game map with the same design pattern as training samples automatically. It can output an abstract label map which can be used in other procedural generator called theme renderer. The impacts of our obtained results show the potential of deep learning methods used in procedural game map generation.

This work supported by Sichuan Science and Technology Program 2019ZDZX0009, 2019ZDZX0005, 2019GFW1116 and 2018GZ0008.

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Author information

Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chenghua, 610051, Sichuan, People’s Republic of China

    Kuang Ping & Luo Dingli

Authors
  1. Kuang Ping
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  2. Luo Dingli
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Corresponding author

Correspondence to Luo Dingli .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Cite this paper

Ping, K., Dingli, L. (2020). Conditional Convolutional Generative Adversarial Networks Based Interactive Procedural Game Map Generation. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1129. Springer, Cham. https://doi.org/10.1007/978-3-030-39445-5_30

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