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.

翻译：我们研究直接从自然语言描述生成设计的任务，并以平面图生成为初始研究领域。语言条件生成模型近来在生成高质量艺术图像方面取得了显著成功。然而，设计必须满足不同于艺术图像生成的约束条件，尤其是空间约束与关系约束。我们通过多项贡献推动该任务的研究。首先，我们提出一个新型数据集 \textit{Tell2Design}（T2D），包含超过8万张与自然语言指令关联的平面图设计。其次，我们提出一个序列到序列模型，可作为未来研究的强基线基准。第三，我们使用多种文本条件图像生成模型对该任务进行基准测试。最后，通过对生成样本进行人工评估并分析人类表现得出结论。我们期望这些贡献能推动语言引导设计生成研究的进步。