This study addresses the nuanced challenge of estimating head translationswithin the context of six-degrees-of-freedom (6DoF) head pose estimation,placing emphasis on this aspect over the more commonly studied head rotations.Identifying a gap in existing methodologies, we recognized the underutilizedpotential synergy between facial geometry and head translation. To bridge thisgap, we propose a novel approach called the head Translation, Rotation, andface Geometry network (TRG), which stands out for its explicit bidirectionalinteraction structure. This structure has been carefully designed to leveragethe complementary relationship between face geometry and head translation,marking a significant advancement in the field of head pose estimation. Ourcontributions also include the development of a strategy for estimatingbounding box correction parameters and a technique for aligning landmarks toimage. Both of these innovations demonstrate superior performance in 6DoF headpose estimation tasks. Extensive experiments conducted on ARKitFace and BIWIdatasets confirm that the proposed method outperforms current state-of-the-arttechniques. Codes are released at https://github.com/asw91666/TRG-Release.