Layout Generation Agents with Large Language Models
This addresses the need for efficient virtual space creation, offering a novel approach but appears incremental as it builds on existing layout generation methods.
The study tackled the problem of automating 3D virtual space creation by proposing an agent-driven layout generation system using GPT-4V, which achieved a high success rate in generating layouts that reflect user instructions.
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.