We present Pro-DG, a framework for procedurally controllable photo-realistic facade generation that combines a procedural shape grammar with diffusion-based image synthesis. Starting from a single input image, we reconstruct its facade layout using grammar rules, then edit that structure through user-defined transformations. As facades are inherently multi-hierarchical structures, we introduce hierarchical matching procedure that aligns facade structures at different levels which is used to introduce control maps to guide a generative diffusion pipeline. This approach retains local appearance fidelity while accommodating large-scale edits such as floor duplication or window rearrangement. We provide a thorough evaluation, comparing Pro-DG against inpainting-based baselines and synthetic ground truths. Our user study and quantitative measurements indicate improved preservation of architectural identity and higher edit accuracy. Our novel method is the first to integrate neuro-symbolically derived shape-grammars for modeling with modern generative model and highlights the broader potential of such approaches for precise and controllable image manipulation.

翻译：我们提出了Pro-DG，一个结合过程式形状语法与基于扩散的图像合成技术、用于实现过程式可控照片级真实感建筑立面生成的框架。从单张输入图像出发，我们首先利用语法规则重建其立面布局，然后通过用户定义的变换对该结构进行编辑。由于立面本质上是多层级结构，我们引入了一种层级匹配过程，用于在不同层级上对齐立面结构，并以此生成控制图来引导生成式扩散流程。该方法在保持局部外观保真度的同时，能够适应大规模编辑，例如楼层复制或窗户重新排列。我们进行了全面评估，将Pro-DG与基于修复的基线方法及合成真实数据进行了比较。我们的用户研究和定量测量结果表明，该方法在建筑特征保持和编辑准确性方面均有提升。这一新颖方法是首次将神经符号派生的形状语法建模与现代生成模型相结合，凸显了此类方法在实现精确可控图像处理方面的更广泛潜力。