ChatHouseDiffusion: Prompt-Guided Generation and Editing of Floor Plans

• URL: http://arxiv.org/abs/2410.11908v1
• Date: Tue, 15 Oct 2024 02:41:46 GMT
• Title: ChatHouseDiffusion: Prompt-Guided Generation and Editing of Floor Plans
• Authors: Sizhong Qin, Chengyu He, Qiaoyun Chen, Sen Yang, Wenjie Liao, Yi Gu, Xinzheng Lu,
• Abstract summary: This paper introduces ChatHouseDiffusion, which leverages large language models (LLMs) to interpret natural language input. It also employs graphormer to encode topological relationships, and uses diffusion models to flexibly generate and edit floor plans. Compared to existing models, ChatHouseDiffusion achieves higher Intersection over Union (IoU) scores, permitting precise, localized adjustments without the need for completes.
• Score: 10.82348603357201
• License:
• Abstract: The generation and editing of floor plans are critical in architectural planning, requiring a high degree of flexibility and efficiency. Existing methods demand extensive input information and lack the capability for interactive adaptation to user modifications. This paper introduces ChatHouseDiffusion, which leverages large language models (LLMs) to interpret natural language input, employs graphormer to encode topological relationships, and uses diffusion models to flexibly generate and edit floor plans. This approach allows iterative design adjustments based on user ideas, significantly enhancing design efficiency. Compared to existing models, ChatHouseDiffusion achieves higher Intersection over Union (IoU) scores, permitting precise, localized adjustments without the need for complete redesigns, thus offering greater practicality. Experiments demonstrate that our model not only strictly adheres to user specifications but also facilitates a more intuitive design process through its interactive capabilities.

Related papers

• HouseLLM: LLM-Assisted Two-Phase Text-to-Floorplan Generation [4.242755827806053]
  This paper proposes a two-phase text-to-floorplan generation method, which guides a Large Language Model (LLM) to generate an initial layout. We incorporate a Chain-of-Thought approach to prompt the LLM based on user text specifications, enabling a more user-friendly and intuitive house layout design. Experimental results demonstrate that our approach achieves state-of-the-art performance across all metrics, validating its effectiveness in practical home design applications.
  arXiv  Detail & Related papers  (2024-11-19T06:57:45Z)
• AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment [13.977849745488339]
  AmoebaLLM is a novel framework designed to enable the instant derivation of large language models of arbitrary shapes. AmoebaLLM significantly facilitates rapid deployment tailored to various platforms and applications.
  arXiv  Detail & Related papers  (2024-11-15T22:02:28Z)
• PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM [58.67882997399021]
  Our research introduces a unified framework for automated graphic layout generation. Our data-driven method employs structured text (JSON format) and visual instruction tuning to generate layouts. We conduct extensive experiments and achieved state-of-the-art (SOTA) performance on public multi-modal layout generation benchmarks.
  arXiv  Detail & Related papers  (2024-06-05T03:05:52Z)
• Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient [52.2669490431145]
  PropEn is inspired by'matching', which enables implicit guidance without training a discriminator. We show that training with a matched dataset approximates the gradient of the property of interest while remaining within the data distribution.
  arXiv  Detail & Related papers  (2024-05-28T11:30:19Z)
• Diffusion Model for Data-Driven Black-Box Optimization [54.25693582870226]
  We focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization. We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons. Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models.
  arXiv  Detail & Related papers  (2024-03-20T00:41:12Z)
• An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-Commerce [22.449320058423886]
  We propose Plug and Play Graph LAnguage Model (PP-GLAM), an explainable ensemble of plug and play models. Our approach uses a modular framework with uniform data processing pipelines. We show that PP-GLAM outperforms several state-of-the-art baselines and a proprietary model on real-world multilingual, multi-regional e-commerce datasets.
  arXiv  Detail & Related papers  (2024-03-01T19:08:25Z)
• Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints [53.66698106829144]
  We propose a unified model to handle a broad range of layout generation tasks. The model is based on continuous diffusion models. Experiment results show that LACE produces high-quality layouts.
  arXiv  Detail & Related papers  (2024-02-07T11:12:41Z)
• Compositional Generative Inverse Design [69.22782875567547]
  Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem. We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
  arXiv  Detail & Related papers  (2024-01-24T01:33:39Z)
• Aligning Large Language Models with Counterfactual DPO [1.8130068086063336]
  This paper explores the utilization of counterfactual prompting to align the model's style without relying on human intervention. We demonstrate that this method effectively instils desirable behaviour, mitigates undesirable ones, and encourages the model to disregard inappropriate instructions.
  arXiv  Detail & Related papers  (2024-01-17T19:43:43Z)
• PROMISE: A Framework for Developing Complex Conversational Interactions (Technical Report) [33.7054351451505]
  We present PROMISE, a framework that facilitates the development of complex language-based interactions with information systems. We show the benefits of PROMISE in the context of application scenarios within health information systems and demonstrate its ability to handle complex interactions.
  arXiv  Detail & Related papers  (2023-12-06T18:59:11Z)
• GroupBERT: Enhanced Transformer Architecture with Efficient Grouped Structures [57.46093180685175]
  We demonstrate a set of modifications to the structure of a Transformer layer, producing a more efficient architecture. We add a convolutional module to complement the self-attention module, decoupling the learning of local and global interactions. We apply the resulting architecture to language representation learning and demonstrate its superior performance compared to BERT models of different scales.
  arXiv  Detail & Related papers  (2021-06-10T15:41:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.