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.

我们引入了一种神经符号变换器模型，使用定制的分割语法将平面、分段的建筑外立面结构转化为程序定义。我们开发了一个半复杂的分割语法，用于建筑外立面，并生成了包含外立面及其相应的程序表达的数据集。通过训练我们的变换器模型使用这个数据集，将分段的平面外立面转化为我们语法的程序语言。在推理过程中，模型应用这个学习到的转换方法到新的外立面分割中，提供了一种用户可以调整以生成不同外立面设计的程序表示。我们的方法不仅自动将静态外立面图像转化为动态、可编辑的程序格式，还增强了设计的灵活性，允许建筑师和设计师进行轻松的修改和变化。我们的方法通过将程序化生成的精确性与神经符号学习的适应性相结合，为外立面设计树立了新的标准。

FacAID: 用于神经符号化外观重建的Transformer模型