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.

介绍了 LightPainter，一种基于草图的重新照明系统，使用两个条件神经网络和一种新型草图模拟过程进行建模，可实现用户交互式改变头像照明效果，且操作简便，实验结果表明该系统具有优秀的针对人像照明的灵活性和高质量，用户更偏好该系统相对于商业照明编辑工具。

LightPainter: 自由手绘实现的交互式人像重照

LightPainter: Interactive Portrait Relighting with Freehand Scribble

Given a portrait image of a person and an environment map of the target
lighting, portrait relighting aims to re-illuminate the person in the image as
if the person appeared in an environment with the target lighting. To achieve
high-quality results, recent methods rely on deep learning. An effective
approach is to supervise the training of deep neural networks with a
high-fidelity dataset of desired input-output pairs, captured with a light
stage. However, acquiring such data requires an expensive special capture rig
and time-consuming efforts, limiting access to only a few resourceful
laboratories. To address the limitation, we propose a new approach that can
perform on par with the state-of-the-art (SOTA) relighting methods without
requiring a light stage. Our approach is based on the realization that a
successful relighting of a portrait image depends on two conditions. First, the
method needs to mimic the behaviors of physically-based relighting. Second, the
output has to be photorealistic. To meet the first condition, we propose to
train the relighting network with training data generated by a virtual light
stage that performs physically-based rendering on various 3D synthetic humans
under different environment maps. To meet the second condition, we develop a
novel synthetic-to-real approach to bring photorealism to the relighting
network output. In addition to achieving SOTA results, our approach offers
several advantages over the prior methods, including controllable glares on
glasses and more temporally-consistent results for relighting videos.

这篇论文提出了一种新方法，通过使用虚拟光源阵列来生成数据集，以代替使用高昂的专用抓取设备，从而实现高质量人像重新照明，同时保持真实感和物理上的一致性。