mlsanityData to 7 Oct 2026

Automating Computational Design with Generative AI

arXiv 2307.02511 ↗First posted 5 Jul 2023cs.LG cs.AIcited by 2

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

AI image generators based on diffusion models have recently garnered attention for their capability to create images from simple text prompts. However, for practical use in civil engineering they need to be able to create specific construction plans for given constraints. This paper investigates the potential of current AI generators in addressing such challenges, specifically for the creation of simple floor plans. We explain how the underlying diffusion-models work and propose novel refinement approaches to improve semantic encoding and generation quality. In several experiments we show that we can improve validity of generated floor plans from 6% to 90%. Based on these results we derive future research challenges considering building information modelling. With this we provide: (i) evaluation of current generative AIs; (ii) propose improved refinement approaches; (iii) evaluate them on various examples; (iv) derive future directions for diffusion models in civil engineering.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 15 of its 49 references that are in the map. First posted on arXiv 5 Jul 2023.

Builds on

15 of 49 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising23 Nov

Other references · 14

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  6. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  7. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
  8. 2021Pivotal Tuning for Latent-based Editing of Real Imagescited by 427
  9. 2021Towards Real-World Blind Face Restoration with Generative Facial Priorcited by 347
  10. 2021GRAND: Graph Neural Diffusioncited by 258
  11. 2022ReCo: Region-Controlled Text-to-Image Generationcited by 177
  12. 2019Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attentioncited by 27
2 more
  1. 2021Building-GAN: Graph-Conditioned Architectural Volumetric Design Generationcited by 20
  2. 2022AI Art in Architecturecited by 12

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2025Text-to-Layout: A Generative Workflow for Drafting Architectural Floor Plans Using LLMscited by 2
  2. 2023Generative AI and the History of Architecturecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Exploring Gen-AI applications in building research and industry: A review
  2. 2025Generative AI for Urban Design: A Stepwise Approach Integrating Human Expertise with Multimodal Diffusion Models
  3. 2024Generative AI Models for Different Steps in Architectural Design: A Literature Review
  4. 2025From Architectural Sketch to Conceptual Representation: Using Structure-Aware Diffusion Model to Generate Renderings of School Buildings
  5. 2024Generating floorplans for various building functionalities via latent diffusion model
  6. 2024CAD-Prompted Generative Models: A Pathway to Feasible and Novel Engineering Designs
  7. 2026Sketch-Based Facade Renovation With Generative AI: A Streamlined Framework for Bypassing As-Built Modelling in Industrial Adaptive Reuse
  8. 2026Directly from Alpha to Omega: Controllable End-to-End Vector Floor Plan Generation ↗