LightPainter：交互式手绘肖像重画
LightPainter: Interactive Portrait Relighting with Freehand Scribble
摘要：
最近的肖像重新照明方法已经实现了肖像照明效果的逼真结果，给出了期望的照明表示，例如环境图。然而，这些方法对于用户交互来说并不直观，并且缺乏精确的照明控制。我们介绍LightPainter，一个基于涂鸦的重新照明系统，允许用户轻松地交互式操纵肖像照明效果。这是通过两个条件神经网络，一个愉快的模块，恢复几何形状和肤色可选条件下，和一个基于涂鸦的模块重新照明。为了训练重新照明模块，我们提出了一种新的涂鸦模拟过程来模仿真实的用户涂鸦，这使得我们的管道进行训练，没有任何人为的注释。我们通过定量和定性实验展示了高质量和灵活的人像照明编辑能力。与商业照明编辑工具的用户研究比较也表明了用户对我们方法的一致偏好。
英文摘要：
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.
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