We introduce FacadeNet, a deep learning approach for synthesizing building facade images from diverse viewpoints. Our method employs a conditional GAN, taking a single view of a facade along with the desired viewpoint information and generates an image of the facade from the distinct viewpoint. To precisely modify view-dependent elements like windows and doors while preserving the structure of view-independent components such as walls, we introduce a selective editing module. This module leverages image embeddings extracted from a pre-trained vision transformer. Our experiments demonstrated state-of-the-art performance on building facade generation, surpassing alternative methods.
@article{arxiv.2311.01240,
title = {FacadeNet: Conditional Facade Synthesis via Selective Editing},
author = {Yiangos Georgiou and Marios Loizou and Tom Kelly and Melinos Averkiou},
journal= {arXiv preprint arXiv:2311.01240},
year = {2023}
}