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FlairGPT: Repurposing LLMs for Interior Designs

2025-01-08 18:01:49
Gabrielle Littlefair, Niladri Shekhar Dutt, Niloy J. Mitra

Abstract

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 this https URL

Abstract (translated)

室内设计涉及精心挑选和布置物品,以创造出美观、实用且和谐的空间,该空间需与客户的设计要求相符合。这一任务特别具有挑战性,因为成功的室内设计方案不仅要将所有必要的物体以一致的风格融入其中,还要确保它们被安排在一种最大化可达性的布局中,并同时考虑多种成本效益及使用因素的影响。虽然有人提出过基于数据的方法来解决这些问题,但这些方法通常局限于特定房间或领域,并且在其最终布局设计考量方面的解释性不足。 本文探讨了大型语言模型(LLMs)能否直接用于室内设计的问题。我们发现尽管目前的LLM尚不能生成完整的平面图,但是可以通过借鉴专业设计师的工作流程,以有组织的方式有效利用它们。通过系统地探究这些大语言模型,可以可靠地产生一系列物品及其放置的相关约束条件。然后将这些信息转化为一个设计布局图,并使用现成的受限制优化设置来解决它,从而生成最终的平面图。 我们在各种设计配置下对我们的算法进行基准测试,与现有的基于LLM的方法和人类设计师的设计进行了比较,并通过多种定量及定性指标以及用户研究评估了结果。总之,我们证明了在有组织的方式使用时,大型语言模型能够有效地产生多样化且高质量的布局方案,使其成为创建大规模虚拟场景的一种可行解决方案。 项目网页:[请访问提供的链接获取更多信息](https://this-url-is-an-example.com)

URL

https://arxiv.org/abs/2501.04648

PDF

https://arxiv.org/pdf/2501.04648.pdf


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