We introduce a neuro-symbolic transformer-based model that converts flat, segmented facade structures into procedural definitions using a custom-designed split grammar. To facilitate this, we first develop a semi-complex split grammar tailored for architectural facades and then generate a dataset comprising of facades alongside their corresponding procedural representations. This dataset is used to train our transformer model to convert segmented, flat facades into the procedural language of our grammar. During inference, the model applies this learned transformation to new facade segmentations, providing a procedural representation that users can adjust to generate varied facade designs. This method not only automates the conversion of static facade images into dynamic, editable procedural formats but also enhances the design flexibility, allowing for easy modifications and variations by architects and designers. Our approach sets a new standard in facade design by combining the precision of procedural generation with the adaptability of neuro-symbolic learning.

翻译：我们提出了一种基于Transformer的神经符号化模型，该模型通过自定义的分割语法将平面化、分割后的立面结构转换为过程式定义。为实现这一目标，我们首先开发了一种专为建筑立面设计的半复杂分割语法，并生成了一个包含立面及其对应过程式表示的数据集。该数据集用于训练我们的Transformer模型，使其能够将分割后的平面立面转换为语法中的过程式语言。在推理阶段，模型将学习到的转换应用于新的立面分割结果，生成可供用户调整以产生多样化立面设计的过程式表示。该方法不仅实现了静态立面图像向动态、可编辑过程式格式的自动转换，还增强了设计灵活性，使建筑师和设计师能够轻松进行修改与变体生成。通过将过程式生成的精确性与神经符号化学习的适应性相结合，我们的方法为立面设计设立了新标准。