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.