Person re-identification (ReID) has achieved rapid improvement recently. However, exploiting the model in a new scene is always faced with huge performance drop. The cause lies in distribution discrepancy between domains, including both low-level (e.g. image quality) and high-level (e.g. pedestrian attribute) variance. To alleviate the problem of domain shift, we propose a novel framework Proxy Task Learning (PTL), which performs body perception tasks on target-domain images while training source-domain ReID, in a multi-task manner. The backbone is shared between tasks and domains, hence both low- and high-level distributions are deeply aligned. We experimentally verify two proxy tasks, i.e. human parsing and attribute recognition, that prominently enhance generalization of the model. When integrating our method into an existing cross-domain pipeline, we achieve state-of-the-art performance on large-scale benchmarks.