Learning to light and relight images

增强现实是将虚拟与现实相结合的过程。为了使组合看起来逼真，虚拟内容必须与真实内容匹配：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。获得高度逼真的效果是可能的(就像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，现在的所有特效都需要数十名，如果不是数百名熟练的艺术家，在每个镜头上煞费苦心地工作。在这项提议中，我们的目标是朝着将虚拟物体真实地插入真实照片的过程自动化迈出一步。具体地说，我们沿着以下两个主轴解决照明估计问题。首先，我们将演示场景的完整、空间变化的照明条件可以从该场景的单个或多个图像中稳健地估计出来。其次，我们将展示如何根据目标照明来交互地重现对象的图像，甚至整个场景。

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.