“Taking Control: Procedural Diffusion Guidance for Architectural Facade Editing” by Plocharski, Swidzinski and Musialski
Conference:
Type(s):
Title:
- Taking Control: Procedural Diffusion Guidance for Architectural Facade Editing
Session/Category Title:
- Images, Video & Computer Vision
Presenter(s)/Author(s):
Abstract:
Our training-free method enables photorealistic facade editing by combining hierarchical procedural structure control with diffusion models. Starting from a facade image, we reconstruct, edit, and guide generation to produce high-fidelity, photorealistic variations. The method ensures structural consistency and appearance preservation, demonstrating the power of symbolic modeling for controllable image synthesis.
References:
[1] Oscar Michel, Anand Bhattad, Eli VanderBilt, Ranjay Krishna, Aniruddha Kembhavi, and Tanmay Gupta. 2023. Diffusion Handles Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D. arXiv preprint arXiv:https://arXiv.org/abs/2307.11073. https://arxiv.org/abs/2307.11073
[2] Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. 2023. Null-text Inversion for Editing Real Images using Guided Diffusion Models. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 6038–6047.
[3] Aleksander Plocharski, Jan Swidzinski, Joanna Porter-Sobieraj, and Przemyslaw Musialski. 2024. FaçAID: A Transformer Model for Neuro-Symbolic Facade Reconstruction. In SIGGRAPH Asia 2024 Conference Papers (Tokyo, Japan) (SA ’24). Association for Computing Machinery, New York, NY, USA, Article 123, 11 pages.
[4] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-Resolution Image Synthesis with Latent Diffusion Models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 10684–10695.
[5] Peter Wonka, Michael Wimmer, François Sillion, and William Ribarsky. 2003. Instant Architecture. ACM Transactions on Graphics (TOG) 22, 3 (2003), 669–677.
[6] Lvmin Zhang and Maneesh Agrawala. 2023. Adding Conditional Control to Text-to-Image Diffusion Models. arXiv preprint arXiv:https://arXiv.org/abs/2302.05543. https://arxiv.org/abs/2302.05543
