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Deep learning applications in games: a survey from a data perspective

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Abstract

This paper presents a comprehensive review of deep learning applications in the video game industry, focusing on how these techniques can be utilized in game development, experience, and operation. As relying on computation techniques, the game world can be viewed as an integration of various complex data. This examines the use of deep learning in processing various types of game data. The paper classifies the game data into asset data, interaction data, and player data, according to their utilization in game development, experience, and operation, respectively. Specifically, this paper discusses deep learning applications in generating asset data such as object images, 3D scenes, avatar models, and facial animations; enhancing interaction data through improved text-based conversations and decision-making behaviors; and analyzing player data for cheat detection and match-making purposes. Although this review may not cover all existing applications of deep learning, it aims to provide a thorough presentation of the current state of deep learning in the gaming industry and its potential to revolutionize game production by reducing costs and improving the overall player experience.

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Data Availibility Statement

No datasets were generated or analysed during the current study

Notes

  1. https://www.midjourney.com/app/

  2. https://openai.com/blog/chatgpt

  3. https://openai.com/blog/openai-codex

  4. Created by Jason M. Allen using the generative Artificial Intelligence platform Midjourney. The painting became a news story when it won the 2022 Colorado State Fair’s annual fine art competition on 5 September, becoming one of the first AI generated images to win such a prize.

  5. https://civitai.com/

  6. https://github.com/AUTOMATIC1111/stable-diffusion-webui

  7. https://en.wikipedia.org/wiki/Electronic_Arts

  8. https://blog.playstation.com/archive/2018/07/09/how-milestone-created-the-breathtaking-bikers-paradise-that-is-strada-della-forra-in-upcoming-ps4-racer-ride-3

  9. http://www.narakathegame.com/

  10. http://www.rockstargames.com/GTAOnline

  11. https://loomai.com

  12. https://pinscreen.com

  13. https://h.163.com

  14. https://ro.my.games/

  15. https://www.youtube.com/watch?v=5R8xZb6J3r0

  16. https://beta.character.ai/

  17. https://en.wikipedia.org/wiki/FIFA_(video_game_series)

  18. https://en.wikipedia.org/wiki/NBA_2K

  19. https://en.wikipedia.org/wiki/Elden_Ring

  20. https://en.wikipedia.org/wiki/God_of_War_(franchise)

  21. https://en.wikipedia.org/wiki/Grand_Theft_Auto_V

  22. https://en.wikipedia.org/wiki/PUBG:_Battlegrounds

  23. https://en.wikipedia.org/wiki/Minecraft

  24. https://en.wikipedia.org/wiki/Honor_of_Kings

  25. https://help.steampowered.com/en/faqs/view/571A-97DA-70E9-FF74

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Acknowledgements

We would like to thank Jiajun Bu and Weixun Wang for their generous and helpful discussions, as well as their constructive suggestions.

Funding

This work is supported by the Key Research and Development Program of Zhejiang Province (No. 2022C01011).

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Authors and Affiliations

  1. Zhejiang University, Hangzhou, 310058, China

    Zhipeng Hu

  2. Netease Fuxi AI Lab, Hangzhou, 310052, China

    Yu Ding, Runze Wu, Lincheng Li, Rongsheng Zhang, Yujing Hu, Feng Qiu, Zhimeng Zhang, Kai Wang, Shiwei Zhao, Yongqiang Zhang, Ji Jiang, Yadong Xi, Jiashu Pu, Wei Zhang, Suzhen Wang, Ke Chen, Tianze Zhou, Jiarui Chen, Yan Song, Tangjie Lv & Changjie Fan

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  1. Zhipeng Hu
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Hu, Z., Ding, Y., Wu, R. et al. Deep learning applications in games: a survey from a data perspective. Appl Intell 53, 31129–31164 (2023). https://doi.org/10.1007/s10489-023-05094-2

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