Architectural photography is a genre of photography that focuses on capturing a building or structure in the foreground with dramatic lighting in the background. Inspired by recent successes in image-to-image translation methods, we aim to perform style transfer for architectural photographs. However, the special composition in architectural photography poses great challenges for style transfer in this type of photographs. Existing neural style transfer methods treat the architectural images as a single entity, which would generate mismatched chrominance and destroy geometric features of the original architecture, yielding unrealistic lighting, wrong color rendition, and visual artifacts such as ghosting, appearance distortion, or color mismatching. In this paper, we specialize a neural style transfer method for architectural photography. Our method addresses the composition of the foreground and background in an architectural photograph in a two-branch neural network that separately considers the style transfer of the foreground and the background, respectively. Our method comprises a segmentation module, a learning-based image-to-image translation module, and an image blending optimization module. We trained our image-to-image translation neural network with a new dataset of unconstrained outdoor architectural photographs captured at different magic times of a day, utilizing additional semantic information for better chrominance matching and geometry preservation. Our experiments show that our method can produce photorealistic lighting and color rendition on both the foreground and background, and outperforms general image-to-image translation and arbitrary style transfer baselines quantitatively and qualitatively. Our code and data are available at https://github.com/hkust-vgd/architectural_style_transfer.
翻译:建筑摄影是一种摄影模式,其重点是在背景中以惊人的光线照明在表面捕捉建筑物或结构。在图像到图像翻译方法的最新成功启发下,我们的目标是对建筑照片进行风格转换。然而,建筑摄影的特殊构成为这类照片的风格转换带来了巨大的挑战。现有的神经风格转换方法将建筑图像作为一个单一实体对待,这将产生不匹配的色度和破坏原始建筑的几何特征,产生不现实的照明、错误的颜色移换和视觉制品,如幽灵、外观扭曲或色彩错配等。在本文件中,我们专门为建筑摄影专门设计一种神经风格转换方法。我们的方法在建筑图片的两层神经网络中处理背景和背景的构成,分别考虑地表和背景的风格转换。我们的方法包括一个分解模块、基于学习的图像到模拟翻译模块,以及一个图像混合化模块。我们在图像到神经风格转换网络上培训了一种神经风格转换方法。我们用新的直观的直观和直观图像转换方法,用一种更精确的图像转换和直观的图像转换方法来制作一个更精确的图像转换。