Abstract

In role-playing games, creators always use specific models to generate non-player characters (NPCs). The prefabricated models are materialized in the scenes, and if more NPCs are included than character models prepared, the players may easily find many NPCs with similar appearances, which makes the scenes unnatural. In this paper, we propose a novel system for generating a rich variety of 3D anime-like NPCs in real time to make the scenes look more natural. We combine a proprietary character customization system with machine learning, where the customizing parameters are treated as feature vectors input in the neural network. The parameters are trained to avoid generating bad-looking models. We demonstrate that the proposed system can generate a natural school classroom scene with a variety of good-looking female student NPCs in a uniform.

More details in [IC-1].

Members

NameAffiliationWeb site
Ruizhe LiKeio University
Masanori NakayamaKeio University

Publications

International conferences/symposiums

  1. Ruizhe LiMasanori NakayamaIssei Fujishiro, “Automatic generation of 3D natural anime-like non-player characters with machine learning,” 2020 International Conference on Cyberworlds, pp. 110—116, Caen, France, September 29—October 1, 2020 [doi: 10.1109/CW49994.2020.00023].

Grants

  1. Grant-in-Aid for Scientific Research (A): 17H00737 (2017―)
  2. Grant-in-Aid for Challenging Research (Pioneering): 19H05576 (2019―)

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