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Tell2Design: A Dataset for Language-Guided Floor Plan Generation

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arxiv 2311.15941 v1 pith:JJNP3FXY submitted 2023-11-27 cs.CL cs.CV

Sicong Leng , Yang Zhou , Mohammed Haroon Dupty , Wee Sun Lee , Sam Conrad Joyce , Wei Lu This is my paper · ORCID
classification cs.CLcs.CV
keywords generationresearchdesignsfloorgeneratinglanguageplantask
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abstract

We consider the task of generating designs directly from natural language descriptions, and consider floor plan generation as the initial research area. Language conditional generative models have recently been very successful in generating high-quality artistic images. However, designs must satisfy different constraints that are not present in generating artistic images, particularly spatial and relational constraints. We make multiple contributions to initiate research on this task. First, we introduce a novel dataset, \textit{Tell2Design} (T2D), which contains more than $80k$ floor plan designs associated with natural language instructions. Second, we propose a Sequence-to-Sequence model that can serve as a strong baseline for future research. Third, we benchmark this task with several text-conditional image generation models. We conclude by conducting human evaluations on the generated samples and providing an analysis of human performance. We hope our contributions will propel the research on language-guided design generation forward.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GRE-Diff: Gaussian Room Embeddings for Structured Layout Diffusion

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Modeling rooms as isotropic Gaussians and using them to initialize and guide diffusion yields controllable, editable polygonal floor plans that beat prior methods on RPLAN similarity and constraint metrics.

  2. FloorPlan-DeepSeek (FPDS): A multimodal approach to floorplan generation using vector-based next room prediction

    cs.CL 2025-06 reject novelty 4.0 of 10

    FPDS adapts the next token prediction idea of large language models to vector-based floor plan generation, predicting one room at a time, but the reported evaluation lacks a quantitative comparison to baselines.

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