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：近期的人像光照方法已经可以根据想要的光照表现（如环境映射）实现逼真的光照效果，但是这些方法对于用户交互不够直观，同时光照控制也不够精确。我们介绍了LightPainter这个基于自由手涂鸦光照渲染系统，使用户能够轻松交互式操作人像光照效果。我们实现了两个有条件的神经网络，一个是美肤模块，可以恢复几何和反照率，同时可能会根据肤色进行控制，另一个是基于涂鸦的光照模块。为了训练光照模块，我们提出了一种新的涂鸦模拟过程来模拟真实用户的涂鸦，这使我们的流水线可以在没有任何人工注释的情况下进行训练。我们通过定量和定性实验展示了高质量和灵活的人像光照编辑能力。与商业光照编辑工具的用户研究比较也展示了一致的用户偏好。