Gaze estimation is a fundamental aspect of many visual tasks. However, the high cost of acquiring gaze datasets with 3D annotations hinders the optimization and application of gaze estimation models. In this work, we propose a novel Head-Eye redirection parametric model based on Neural Radiance Field. This model allows for dense gaze data generation with view consistency and accurate gaze direction. Furthermore, our head-eye redirection parametric model can decouple the face and eyes for separate neural rendering, which enables us to separately control the attributes of the face, identity, illumination, and eye gaze direction. As a result, diverse 3D-aware gaze datasets can be obtained by manipulating the latent code belonging to different face attributes in an unsupervised manner. Our method has achieved state-of-the-art performance in image quality and accuracy gaze annotations compared with existing gaze data synthesis methods. Extensive experiments on several benchmarks demonstrate that our method can effectively improve domain generalization and domain adaptation in the gaze estimation task.