  Inspired by cognitive theories, we introduce AnyHome, a framework that
translates any text into well-structured and textured indoor scenes at a
house-scale. By prompting Large Language Models (LLMs) with designed templates,
our approach converts provided textual narratives into amodal structured
representations. These representations guarantee consistent and realistic
spatial layouts by directing the synthesis of a geometry mesh within defined
constraints. A Score Distillation Sampling process is then employed to refine
the geometry, followed by an egocentric inpainting process that adds lifelike
textures to it. AnyHome stands out with its editability, customizability,
diversity, and realism. The structured representations for scenes allow for
extensive editing at varying levels of granularity. Capable of interpreting
texts ranging from simple labels to detailed narratives, AnyHome generates
detailed geometries and textures that outperform existing methods in both
quantitative and qualitative measures.
