MagicItem: Dynamic Behavior Design of Virtual Objects with Large Language Models in a Consumer Metaverse Platform
<|reference_start|>MagicItem: Dynamic Behavior Design of Virtual Objects with Large Language Models in a Consumer Metaverse Platform: To create rich experiences in virtual reality (VR) environments, it is essential to define the behavior of virtual objects through programming. However, programming in 3D spaces requires a wide range of background knowledge and programming skills. Although Large Language Models (LLMs) have provided programming support, they are still primarily aimed at programmers. In metaverse platforms, where many users inhabit VR spaces, most users are unfamiliar with programming, making it difficult for them to modify the behavior of objects in the VR environment easily. Existing LLM-based script generation methods for VR spaces require multiple lengthy iterations to implement the desired behaviors and are difficult to integrate into the operation of metaverse platforms. To address this issue, we propose a tool that generates behaviors for objects in VR spaces from natural language within Cluster, a metaverse platform with a large user base. By integrating LLMs with the Cluster Script provided by this platform, we enable users with limited programming experience to define object behaviors within the platform freely. We have also integrated our tool into a commercial metaverse platform and are conducting online experiments with 63 general users of the platform. The experiments show that even users with no programming background can successfully generate behaviors for objects in VR spaces, resulting in a highly satisfying system. Our research contributes to democratizing VR content creation by enabling non-programmers to design dynamic behaviors for virtual objects in metaverse platforms.<|reference_end|>
@article{kurai2024magicitem:,
    title={MagicItem: Dynamic Behavior Design of Virtual Objects with Large
  Language Models in a Consumer Metaverse Platform},
    author={Ryutaro Kurai, Takefumi Hiraki, Yuichi Hiroi, Yutaro Hirao, Monica
  Perusquia-Hernandez, Hideaki Uchiyama and Kiyoshi Kiyokawa},
    journal={arXiv preprint arXiv:2406.13242},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13242},
    primaryClass={cs.HC}
}