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：我们提出了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。该方法采用条件生成对抗网络（conditional GAN），以单个立面视图及所需的视角信息为输入，从指定视角生成该立面的图像。为精准修改窗户、门等视角依赖性元素，同时保留墙壁等视角无关组件的结构，我们引入了一个选择性编辑模块。该模块利用从预训练视觉Transformer（vision transformer）中提取的图像嵌入。实验结果表明，本方法在建筑立面生成任务上取得了超越其他方法的先进性能。