# Eliminating Rasterization: Direct Vector Floor Plan Generation with DiffPlanner

arXiv: https://arxiv.org/abs/2508.13738 · HTML: https://mlsanity.com/paper/2508.13738/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2025.

## Built on by (most cited first, 4 of 4)

- [Tokenization Allows Multimodal Large Language Models to Understand, Generate and Edit Architectural Floor Plans](https://mlsanity.com/paper/2603.11640/) (2026)
- [SVG Decomposition for Enhancing Large Multimodal Models Visualization Comprehension: A Study with Floor Plans](https://mlsanity.com/paper/2511.03478/) (2025)
- [TLC-Plan: A Two-Level Codebook Based Network for End-to-End Vector Floorplan Generation](https://mlsanity.com/paper/2602.07100/) (2026)
- [HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation](https://mlsanity.com/paper/2605.18932/) (2026)

## Abstract (arXiv)

The boundary-constrained floor plan generation problem aims to generate the topological and geometric properties of a set of rooms within a given boundary. Recently, learning-based methods have made significant progress in generating realistic floor plans. However, these methods involve a workflow of converting vector data into raster images, using image-based generative models, and then converting the results back into vector data. This process is complex and redundant, often resulting in information loss. Raster images, unlike vector data, cannot scale without losing detail and precision. To address these issues, we propose a novel deep learning framework called DiffPlanner for boundary-constrained floor plan generation, which operates entirely in vector space. Our framework is a Transformer-based conditional diffusion model that integrates an alignment mechanism in training, aligning the optimization trajectory of the model with the iterative design processes of designers. This enables our model to handle complex vector data, better fit the distribution of the predicted targets, accomplish the challenging task of floor plan layout design, and achieve user-controllable generation. We conduct quantitative comparisons, qualitative evaluations, ablation experiments, and perceptual studies to evaluate our method. Extensive experiments demonstrate that DiffPlanner surpasses existing state-of-the-art methods in generating floor plans and bubble diagrams in the creative stages, offering more controllability to users and producing higher-quality results that closely match the ground truths.