Head pose estimation aims at predicting an accurate pose from an image. Current approaches rely on supervised deep learning, which typically requires large amounts of labeled data. Manual or sensor-based annotations of head poses are prone to errors. A solution is to generate synthetic training data by rendering 3D face models. However, the differences (domain gap) between rendered (source-domain) and real-world (target-domain) images can cause low performance. Advances in visual domain adaptation allow reducing the influence of domain differences using adversarial neural networks, which match the feature spaces between domains by enforcing domain-invariant features. While previous work on visual domain adaptation generally assumes discrete and shared label spaces, these assumptions are both invalid for pose estimation tasks. This work is the first to present domain adaptation for head pose estimation with a focus on partially shared and continuous label spaces. More precisely, the method adapts the predominant weighting approaches to continuous label spaces by applying a weighted resampling of the source domain during training. To evaluate the approach, the authors revise and extend existing datasets resulting in a new benchmark for visual domain adaption. Their experiments show that the proposed method improves the accuracy of head pose estimation for real-world images despite using only labels from synthetic images.