CHATDESIGN: BOOTSTRAPPING GENERATIVE FLOOR PLAN DESIGN WITH PRE-TRAINED LARGE LANGUAGE MODELS

Large language models (LLMs) have achieved remarkable success in various domains, revolutionizing tasks such as language translation, text generation, and question-answering. However, generating floor plan designs poses a unique challenge that demands the fulfilment of intricate spatial and relational constraints. In this paper, we propose ChatDesign, an innovative approach that leverages the power of pre-trained LLMs to generate floor plan designs from natural language descriptions, while incorporating iterative modifications based on user interaction. By processing user input text through a pre-trained LLM and utilizing a decoder, we can generate regression parameters and floor plans that are precisely tailored to satisfy the specific needs of the user. Our approach incorporates an iterative refinement process, optimizing the model output by considering the input text and previous results. Throughout these interactions, we employ many strategic techniques to ensure the generated design images align precisely with the user's requirements. The proposed approach is extensively evaluated through rigorous experiments, including user studies, demonstrating its feasibility and efficacy. The empirical results consistently demonstrate the superiority of our method over existing approaches, showcasing its ability to generate floor plans that rival those created by human designer. Our code will be available at https://github.com/THUKingmin/ChatDesign.