InteriorAgent: LLM Agent for Interior Design-Aware 3D Layout Generation

Creating interior layout designs has numerous applications, including virtual reality, architectural visualization and real estate planning. Generating realistic and functional indoor scenes requires a nuanced understanding of spatial configurations and human-centered design principles. We propose InteriorAgent, an LLM-agent-driven framework for text-to-3D indoor scene generation that produces scenes with visual quality and functional utility that significantly surpass prior works. We achieve this through several key advantages of InteriorAgent: (1) encoding of interior design principles with a novel scene description language, (2) aesthetics and functionality through synthesis tools that satisfy design principles, (3) realism and prompt adherence with optimization tools that ensure ergonomics and iterative constraint satisfaction, (4) extensibility with a framework that allows incorporating even mature, complex tools like diffusion models, LLMs and 3D generation repositories. We evaluate InteriorAgent through a user study, where participants strongly favor its generated scenes over prior state-of-the-art methods. Additionally, we demonstrate novel applications uniquely enabled by InteriorAgent, including language-based scene editing and seamless tool integration for new tasks. Code and data will be publicly released.