AnyHome：开放词汇的结构化与纹理化3D房屋生成
AnyHome: Open-Vocabulary Generation of Structured and Textured 3D Homes
AI总结：
AnyHome框架利用LLMs将文本转化为非模态结构化表示，结合Score Distillation Sampling和自我中心修复技术，生成可编辑、多样化的房屋规模3D室内场景，在定量和定性指标上优于现有方法。
摘要：
受认知理论启发，我们提出了AnyHome框架，能将任何文本转化为房屋规模的结构良好且带有纹理的室内场景。通过使用设计的模板提示大型语言模型（LLMs），我们的方法将提供的文本叙述转化为非模态结构化表示。这些表示通过在定义的约束内指导几何网格的合成，保证了空间布局的一致性和真实性。随后采用Score Distillation Sampling过程来细化几何，接着通过以自我为中心的修复过程为其添加逼真的纹理。AnyHome以其可编辑性、可定制性、多样性和真实性脱颖而出。场景的结构化表示允许在不同粒度级别进行广泛编辑。AnyHome能够解释从简单标签到详细叙述的文本，生成的详细几何和纹理在定量和定性指标上均优于现有方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
