We introduce a co-designed approach for human portrait relighting that
combines a physics-guided architecture with a pre-training framework. Drawing
on the Cook-Torrance reflectance model, we have meticulously configured the
architecture design to precisely simulate light-surface interactions.
Furthermore, to overcome the limitation of scarce high-quality lightstage data,
we have developed a self-supervised pre-training strategy. This novel
combination of accurate physical modeling and expanded training dataset
establishes a new benchmark in relighting realism.

引入一种协同设计方法，用于人像重照，将物理引导的架构与预训练框架相结合。通过基于 Cook-Torrance 反射模型的精心配置的架构设计，准确模拟光 - 表面相互作用。此外，为了克服高质量光台数据稀缺的限制，我们开发了一种自监督预训练策略。这种精确的物理建模和扩展的训练数据集的新结合为重照现实主义建立了新的基准。

SwitchLight: 基於物理驅動架構和預訓練框架的人物肖像輝光共設計

SwitchLight: Co-design of Physics-driven Architecture and Pre-training  Framework for Human Portrait Relighting

To simultaneously address the rising need of expressing uncertainties in deep
learning models along with producing model outputs which are consistent with
the known scientific knowledge, we propose a novel physics-guided architecture
(PGA) of neural networks in the context of lake temperature modeling where the
physical constraints are hard coded in the neural network architecture. This
allows us to integrate such models with state of the art uncertainty estimation
approaches such as Monte Carlo (MC) Dropout without sacrificing the physical
consistency of our results. We demonstrate the effectiveness of our approach in
ensuring better generalizability as well as physical consistency in MC
estimates over data collected from Lake Mendota in Wisconsin and Falling Creek
Reservoir in Virginia, even with limited training data. We further show that
our MC estimates correctly match the distribution of ground-truth observations,
thus making the PGA paradigm amenable to physically grounded uncertainty
quantification.

在深度学习模型中硬编码物理约束的物理引导架构（PGA）可同时解决表达不确定性和生成与已知科学知识一致的模型输出的需求，加入蒙特卡罗（MC）Dropout 等最先进的不确定性估计方法，以确保 MC 估计能够正确匹配地面观测分布，从而实现更好的可泛化性和物理一致性。