Single image scene relighting aims to generate a realistic new version of an input image so that it appears to be illuminated by a new target light condition. Although existing works have explored this problem from various perspectives, generating relit images under arbitrary light conditions remains highly challenging, and related datasets are scarce. Our work addresses this problem from both the dataset and methodological perspectives. We propose two new datasets: a synthetic dataset with the ground truth of intrinsic components and a real dataset collected under laboratory conditions. These datasets alleviate the scarcity of existing datasets. To incorporate physical consistency in the relighting pipeline, we establish a two-stage network based on intrinsic decomposition, giving outputs at intermediate steps, thereby introducing physical constraints. When the training set lacks ground truth for intrinsic decomposition, we introduce an unsupervised module to ensure that the intrinsic outputs are satisfactory. Our method outperforms the state-of-the-art methods in performance, as tested on both existing datasets and our newly developed datasets. Furthermore, pretraining our method or other prior methods using our synthetic dataset can enhance their performance on other datasets. Since our method can accommodate any light conditions, it is capable of producing animated results. The dataset, method, and videos are publicly available.

翻译：单张图像场景重光照旨在生成输入图像的真实新版本，使其看起来是在新的目标光照条件下被照亮的。尽管现有研究已从不同角度探讨了这一问题，但在任意光照条件下生成重光照图像仍然极具挑战性，且相关数据集稀缺。本研究从数据集和方法论两个层面应对这一挑战。我们提出了两个新数据集：一个包含本征分量真值的合成数据集，以及一个在实验室条件下采集的真实数据集。这些数据集缓解了现有数据集的匮乏问题。为了在重光照流程中融入物理一致性，我们构建了一个基于本征分解的两阶段网络，该网络在中间步骤输出结果，从而引入了物理约束。当训练集缺乏本征分解的真值时，我们引入了一个无监督模块以确保本征输出的质量。在现有数据集和我们新开发的数据集上的测试表明，我们的方法在性能上超越了现有最优方法。此外，使用我们的合成数据集对我们的方法或其他现有方法进行预训练，能够提升它们在其他数据集上的表现。由于我们的方法能够适应任意光照条件，因此能够生成动画效果的结果。数据集、方法及相关视频均已公开。