Lighting is a determining factor in photography that affects the style, expression of emotion, and even quality of images. Creating or finding satisfying lighting conditions, in reality, is laborious and time-consuming, so it is of great value to develop a technology to manipulate illumination in an image as post-processing. Although previous works have explored techniques based on the physical viewpoint for relighting images, extensive supervisions and prior knowledge are necessary to generate reasonable images, restricting the generalization ability of these works. In contrast, we take the viewpoint of image-to-image translation and implicitly merge ideas of the conventional physical viewpoint. In this paper, we present an Illumination-Aware Network (IAN) which follows the guidance from hierarchical sampling to progressively relight a scene from a single image with high efficiency. In addition, an Illumination-Aware Residual Block (IARB) is designed to approximate the physical rendering process and to extract precise descriptors of light sources for further manipulations. We also introduce a depth-guided geometry encoder for acquiring valuable geometry- and structure-related representations once the depth information is available. Experimental results show that our proposed method produces better quantitative and qualitative relighting results than previous state-of-the-art methods. The code and models are publicly available on https://github.com/NK-CS-ZZL/IAN.
翻译:光照是影响图像风格、情感表达甚至质量的一个决定性因素。 在现实中,创造或寻找满足的照明条件既费力又费时,因此,开发一种技术,在图像处理后,在图像中操作光照,是很有价值的。虽然以前的工作探索了以物理观点为基础的技术,以光照图像,但为了产生合理的图像,需要广泛的监督和先前的知识,以限制这些作品的概括性能力。相比之下,我们采用了图像到图像翻译的观点,并隐含地将传统物理观点的概念合并在一起。在本文中,我们展示了一个从等级取样到以高效的方式从单一图像中逐渐重新点亮一个场景的灯光软件网络(IAN)。此外,一个光化-Award-Award 遗留区(IARB) 的设计旨在接近物理转换过程,并提取精确的光源描述器,供进一步操作。我们还引入了深度导测制的几何和结构模型,一旦有了深度信息,我们遵循了从层次取样到深度取样/光学模型后,我们就可以使用的方法。实验性结果显示比公开模型更好的质量/数字/结果。