Learning to light and relight images

增强现实是虚拟与真实的相结合的过程。为了使组合看起来逼真，虚拟内容必须与真实的相匹配：它们在场景中的方向、照明和存在都必须与真实世界的条件相匹配。实现高度逼真的效果是可能的（正如在大片中经常展示的那样），但这样做需要大量的人力-事实上，现在所有的特效都需要几十个，如果不是几百个，熟练的艺术家在每个镜头上辛勤工作。在这个建议中，我们的目标是采取一个步骤，自动化的过程中，现实地插入虚拟对象到真实的照片。具体而言，我们解决的问题，照明估计沿着以下两个主轴。首先，我们将证明，一个场景的完整的，空间变化的照明条件，可以鲁棒地估计从一个单一的，或多个图像的场景。其次，我们将展示如何根据目标照明交互式地重新照亮对象甚至整个场景的图像。

Augmented reality is the process of combining the virtual with the real. For the combination to look realistic, the virtual content must match the real: their orientation, lighting, and presence in the scene must all match the real-world conditions. Achieving highly realistic effects is possible (as demonstrated on a regular basis in blockbuster movies), but doing so requires tremendous amounts of manpower---indeed, all special effects nowadays require tens, if not hundreds, of skilled artists painstakingly working on each shot. In this proposal, we aim at taking a step towards automating the process of realistically inserting virtual objects into real photographs. Specifically, we address the problem of lighting estimation along the following two main axes. First, we will demonstrate that the full, spatially-varying lighting conditions of a scene can be robustly estimated from either a single, or multiple images of that scene. Second, we will show how images of objects and even full scenes can be interactively relit according to a target lighting.