标题： FlairGPT：将大型语言模型用于室内设计的再利用 显示英文标题

标题： FlairGPT: Repurposing LLMs for Interior Designs

摘要： 室内设计涉及对物体的精心选择和布置，以创建一个美观、功能性强且协调的空间，符合客户的设计方案。 这项任务尤其具有挑战性，因为成功的设计不仅要以统一的风格包含所有必要的物体，还要确保它们以最大化可达性的方式布置，同时遵守各种可负担性和使用考虑因素。 已经提出了数据驱动的解决方案，但这些方案通常是针对特定房间或领域的，并且在生成最终布局时使用的设计考虑缺乏可解释性。 在本文中，我们研究大型语言模型（LLMs）是否可以直接用于室内设计。 虽然我们发现LLMs目前还无法生成完整的布局，但可以通过一种结构化的方法有效地加以利用，这种方法受到室内设计师工作流程的启发。 通过系统地探测LLMs，我们可以可靠地生成一个包含相关约束条件的物体列表，这些约束条件指导它们的放置。 我们将这些信息转化为一个设计布局图，然后使用现成的约束优化设置来生成最终的布局。 我们在各种设计配置中将我们的算法与现有的基于LLM的方法和人类设计进行基准测试，并通过多种定量和定性指标以及用户研究来评估结果。 总之，我们证明了当以结构化方式使用时，LLMs可以有效地生成多样化高质量的布局，使其成为创建大规模虚拟场景的可行解决方案。 项目网页地址为 https://flairgpt.github.io/ 显示英文摘要

摘要： Interior design involves the careful selection and arrangement of objects to create an aesthetically pleasing, functional, and harmonized space that aligns with the client's design brief. This task is particularly challenging, as a successful design must not only incorporate all the necessary objects in a cohesive style, but also ensure they are arranged in a way that maximizes accessibility, while adhering to a variety of affordability and usage considerations. Data-driven solutions have been proposed, but these are typically room- or domain-specific and lack explainability in their design design considerations used in producing the final layout. In this paper, we investigate if large language models (LLMs) can be directly utilized for interior design. While we find that LLMs are not yet capable of generating complete layouts, they can be effectively leveraged in a structured manner, inspired by the workflow of interior designers. By systematically probing LLMs, we can reliably generate a list of objects along with relevant constraints that guide their placement. We translate this information into a design layout graph, which is then solved using an off-the-shelf constrained optimization setup to generate the final layouts. We benchmark our algorithm in various design configurations against existing LLM-based methods and human designs, and evaluate the results using a variety of quantitative and qualitative metrics along with user studies. In summary, we demonstrate that LLMs, when used in a structured manner, can effectively generate diverse high-quality layouts, making them a viable solution for creating large-scale virtual scenes. Project webpage at https://flairgpt.github.io/