Head pose estimation is a crucial problem that involves the prediction of the Euler angles of a human head in an image. Previous approaches predict head poses through landmarks detection, which can be applied to multiple downstream tasks. However, previous landmark-based methods can not achieve comparable performance to the current landmark-free methods due to lack of modeling the complex nonlinear relationships between the geometric distribution of landmarks and head poses. Another reason for the performance bottleneck is that there exists biased underlying distribution of the 3D pose angles in the current head pose benchmarks. In this work, we propose OsGG-Net, a One-step Graph Generation Network for estimating head poses from a single image by generating a landmark-connection graph to model the 3D angle associated with the landmark distribution robustly. To further ease the angle-biased issues caused by the biased data distribution in learning the graph structure, we propose the UnBiased Head Pose Dataset, called UBHPD, and a new unbiased metric, namely UBMAE, for unbiased head pose estimation. We conduct extensive experiments on various benchmarks and UBHPD where our method achieves the state-of-the-art results in terms of the commonly-used MAE metric and our proposed UBMAE. Comprehensive ablation studies also demonstrate the effectiveness of each part in our approach.