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Tell2Design: A Dataset for Language-Guided Floor Plan Generation (2311.15941v1)

Published 27 Nov 2023 in cs.CL and cs.CV

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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Analysis of "Tell2Design: A Dataset for Language-Guided Floor Plan Generation"

The paper "Tell2Design: A Dataset for Language-Guided Floor Plan Generation" addresses a significant advancement in the domain of design generation from natural language descriptions—a task that has not been extensively explored. This paper not only introduces a novel dataset, Tell2Design (T2D), but it also proposes a Sequence-to-Sequence (Seq2Seq) model that provides a robust baseline for future research in language-guided design.

Key Contributions

  1. Novel Dataset: The T2D dataset consists of over 80,000 floor plan designs paired with natural language instructions. This dataset fills a crucial gap in the area of design generation by enabling the exploration of design tasks driven directly by language inputs.
  2. Seq2Seq Baseline Model: The authors present a Sequence-to-Sequence model that interprets natural language instructions to generate floor plans, demonstrating a practical approach to this new form of design generation.
  3. Evaluation and Comparative Analysis: The paper benchmarks this task against several text-conditional image generation models including Obj-GAN, CogView, and Imagen. The evaluation highlights the strengths and limitations of these models in handling the precision required by floor plan generation.

Strong Numerical Results

The proposed T2D model achieves a macro IoU of 54.34, significantly outperforming other methods. This indicates the model's ability to align generated designs with the detailed requirements specified in the input text. The integration of boundary information into the Seq2Seq approach notably enhances model performance, showing its efficacy in addressing spatial constraints.

Implications and Future Directions

The research has crucial implications for both theoretical and practical advancements in AI-driven design tasks. By framing floor plan generation as a guided process based on natural language, this paper opens new pathways for interactive design systems. Potential future developments could include:

  • Improving Robustness: Enhancing robustness in understanding diverse and sometimes ambiguous human instructions remains a vital area for improvement.
  • Design Diversity: Incorporating mechanisms to diversify output designs, reflecting the inherent variability in possible solutions, could provide broader applicability.
  • Domain Extension: Exploring language-guided design in other domains such as document layouts or UI design could significantly broaden the impact of this research.

Overall, this paper sets a foundation for further exploration into language-guided design and its integration into practical AI applications, stressing the importance of accommodating the complexity and precision necessitated by real-world design constraints.

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Authors (6)
  1. Sicong Leng (15 papers)
  2. Yang Zhou (311 papers)
  3. Mohammed Haroon Dupty (12 papers)
  4. Wee Sun Lee (60 papers)
  5. Sam Conrad Joyce (4 papers)
  6. Wei Lu (325 papers)
Citations (4)
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GitHub

  1. GitHub - lucidrains/imagen-pytorch: Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch (7,839 stars)