Recent portrait relighting methods have achieved realistic results of portrait lighting effects given a desired lighting representation such as an environment map. However, these methods are not intuitive for user interaction and lack precise lighting control. We introduce LightPainter, a scribble-based relighting system that allows users to interactively manipulate portrait lighting effect with ease. This is achieved by two conditional neural networks, a delighting module that recovers geometry and albedo optionally conditioned on skin tone, and a scribble-based module for relighting. To train the relighting module, we propose a novel scribble simulation procedure to mimic real user scribbles, which allows our pipeline to be trained without any human annotations. We demonstrate high-quality and flexible portrait lighting editing capability with both quantitative and qualitative experiments. User study comparisons with commercial lighting editing tools also demonstrate consistent user preference for our method.

翻译：近期人像重光照方法在给定环境贴图等期望光照表示时已能实现逼真的光照效果，但这些方法缺乏直观的用户交互性及精确的光照控制能力。我们提出基于涂鸦的重光照系统LightPainter，使用户能够便捷地交互式调整人像光照效果。该系统通过两个条件神经网络实现：一个去光照模块，可基于肤色条件恢复几何与反照率信息；一个基于涂鸦的重光照模块。为训练重光照模块，我们提出新颖的涂鸦模拟流程以模仿真实用户涂鸦，使整个管线无需任何人工标注即可完成训练。定量与定性实验均证明了该方法具备高质量且灵活的人像光照编辑能力。与商业光照编辑工具的用户对比研究也显示，用户持续倾向选择我们的方法。