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GAN applied to Wave Function Collapse for procedural map generation

Published:25 July 2022Publication History

ABSTRACT

This paper describes the use of Generative Adversarial Network (GAN) applied to the Wave Function Collapse (WFC) algorithm for procedural content generation. The goal of this system is to enable level designers to generate coherent 3D worlds with brand new meshes generated by the GAN.

References

  1. Nordvig Møller, T., Billeskov, J., & Palamas, G. (2020, September). Expanding wave function collapse with growing grids for procedural map generation. In International Conference on the Foundations of Digital Games (pp. 1-4)Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Mintrone, A., & Erioli, A. (2021). Training Spaces-Fostering machine sensibility for spatial assemblages through wave function collapse and reinforcement learning.Google ScholarGoogle ScholarCross RefCross Ref
  3. Wang, X., Xu, D., & Gu, F. (2020). 3D model inpainting based on 3D deep convolutional generative adversarial network. IEEE Access, 8, 170355-170363.Google ScholarGoogle ScholarCross RefCross Ref
  4. Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., & Xiao, J. (2015). 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computerGoogle ScholarGoogle Scholar
  5. B. Yang, S. Rosa, A. Markham, N. Trigoni and H. Wen, ”Dense 3D Object Reconstruction from a Single Depth View,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 12, pp. 2820-2834, 1 Dec. 2019, doi: 10.1109/TPAMI.2018.2868195.Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

    cover image ACM Conferences
    SIGGRAPH '22: ACM SIGGRAPH 2022 Posters
    July 2022
    132 pages
    ISBN:9781450393614
    DOI:10.1145/3532719

    Copyright © 2022 Owner/Author

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 25 July 2022

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    Qualifiers

    • poster
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate1,822of8,601submissions,21%

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