Abstract:We use hierarchical procedural rules for the generation of control maps within the stable diffusion framework to produce photo-realistic architectural facade images. Starting from a single input image and its segmentation, we apply an inverse procedural module to identify the facade's hierarchical layout. Leveraging this hierarchy and structural features, we introduce a novel ControlNet pipeline that generates new facade imagery guided by procedural transformations. Our method enables various structural edits, including floor duplication and window rearrangement, by integrating hierarchical alignment directly into control maps. This precisely guides the diffusion-based generative process, ensuring local appearance fidelity alongside extensive structural modifications. Comprehensive evaluations, including comparisons with inpainting-based approaches and synthetic benchmarks, confirm our approach's superior capability in preserving architectural identity and achieving accurate, controllable edits. Quantitative results and user feedback validate our method's effectiveness.
| Comments: | 17 pages, 15 figures, Computer Graphics Forum 2026 Journal Paper |
| Subjects: | Graphics (cs.GR); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| ACM classes: | I.3.7; I.4.9; I.2.10 |
| Cite as: | arXiv:2504.01571 [cs.GR] |
| (or arXiv:2504.01571v2 [cs.GR] for this version) | |
| https://doi.org/10.48550/arXiv.2504.01571 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1111/cgf.70487
DOI(s) linking to related resources |
Submission history
From: Aleksander Plocharski [view email]
[v1]
Wed, 2 Apr 2025 10:16:19 UTC (33,340 KB)
[v2]
Thu, 14 May 2026 15:23:21 UTC (34,256 KB)