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