Title: Pro-DG: Procedural Diffusion Guidance for Architectural Facade Generation Show Chinese title

Title: 面向建筑立面生成的程序扩散引导（Pro-DG）

Abstract: 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. Show Chinese abstract

Abstract: 我们提出了Pro-DG，这是一种用于程序化可控的现实主义立面生成的框架，它结合了基于过程的形状文法与基于扩散的图像合成技术。 从单一输入图像开始，我们利用文法规则重建其立面布局，然后通过用户定义的变换编辑该结构。 由于立面本质上是多层级结构，我们引入了一种分层匹配程序，用于对齐不同层级的立面结构，这被用来引入控制图来引导生成扩散管道。 这种方法在保留局部外观保真度的同时，能够适应大规模编辑，如楼层复制或窗户重新排列。 我们进行了全面评估，将Pro-DG与基于填补的方法和合成真实数据进行比较。 我们的用户研究和定量测量表明，Pro-DG在保留建筑身份和提高编辑准确性方面表现更优。 我们的新方法首次集成了由神经符号衍生出的形状文法以建模，并结合现代生成模型，展示了此类方法在精确且可控的图像操作中的广泛潜力。