In recent years, there has been an increasing demand for customizable 3D
virtual spaces. Due to the significant human effort required to create these
virtual spaces, there is a need for efficiency in virtual space creation. While
existing studies have proposed methods for automatically generating layouts
such as floor plans and furniture arrangements, these methods only generate
text indicating the layout structure based on user instructions, without
utilizing the information obtained during the generation process. In this
study, we propose an agent-driven layout generation system using the GPT-4V
multimodal large language model and validate its effectiveness. Specifically,
the language model manipulates agents to sequentially place objects in the
virtual space, thus generating layouts that reflect user instructions.
Experimental results confirm that our proposed method can generate virtual
spaces reflecting user instructions with a high success rate. Additionally, we
successfully identified elements contributing to the improvement in behavior
generation performance through ablation study.

研究了使用 GPT-4V 多模态大语言模型的基于代理的布局生成系统，通过顺序放置对象在虚拟空间中生成反映用户指令的布局，实验证实该方法能以高成功率生成反映用户指令的虚拟空间，并通过消融研究成功地确定了对行为生成性能改善做出贡献的要素。