Conditional Convolutional Generative Adversarial Networks Based Interactive Procedural Game Map Generation

  • Kuang Ping
  • Luo DingliEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1129)


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.


Generative adversarial networks Procedural generation Convolutional neural networks 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Electronic Science and Technology of ChinaChenghuaPeople’s Republic of China

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