MHPE: Learning Morphology Relationships for Robust Head Pose Estimation with Facial Rotation Representation

Although accurate head pose estimation is critical for natural human-computer interaction, it remains challenging due to occlusion, extreme poses, illumination conditions, and data ambiguity issues. To address these challenges, a novel morphology aware Transformer framework (MHPE) is proposed, which can learn morphological relationships during facial rotation. The methodology is based on two key findings: cross-region geometric dependencies and angle-specific morphodynamic representations. The proposed framework incorporates two key components: adversarial feature generation, which generates robust rotation representations by adaptive multi-scale feature interaction; and morphology relationship inference, which establishes long-range dependencies between facial features through a cross-modal attention mechanism that incorporates morphological priors. Extensive evaluations on three demanding benchmarks (BIWI, AFLW2000, and 300W-LP) demonstrate state-of-the-art performance, particularly in demanding scenarios. The Python implementation will be available on request to facilitate reproducibility.