Abstract
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.
Abstract (translated)
最近肖像重曝方法已经实现了给定环境地图所需的理想照明效果的逼真结果。然而，这些方法对用户交互不够直观，缺乏精确的照明控制。我们介绍了光画者(LightPainter)，一种基于涂画系统的重曝系统，使用户可以轻松地交互式操纵肖像照明效果。这通过两个条件神经网络实现，一个令人愉悦模块可以从肤色可选地恢复几何和反射，另一个是基于涂画系统的重曝模块。为了训练重曝模块，我们提出了一种独特的涂画模拟程序，以模拟真实的用户涂画，从而使我们的管道无需人类标注就可以训练。我们通过定量和定性实验展示了高质量的和灵活的肖像照明编辑能力。用户研究比较了我们方法的商业照明编辑工具也证明了用户对这种方法的一致性偏好。
URL
https://arxiv.org/abs/2303.12950
