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.

翻译：我们提出FacadeNet，一种用于从不同视角合成建筑立面图像的深度学习方法。该方法采用条件式生成对抗网络（conditional GAN），以单视角立面图像和期望视角信息作为输入，生成该立面在目标视角下的图像。为精确修改窗户、门等视角依赖元素，同时保持墙体等视角无关组件的结构，我们引入选择性编辑模块。该模块利用从预训练视觉Transformer（vision transformer）中提取的图像嵌入。实验表明，本方法在建筑立面生成任务上达到最优性能，显著优于现有替代方法。