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STAG-Net: Lightweight Head Pose Estimation with Structure-Aware Attention

STAG-Net is a new lightweight head pose estimation method that uses structure-aware attention over landmark groups, achieving state-of-the-art accuracy among landmark-based approaches on standard benchmarks. It requires only 0.012 to 0.043 million parameters and 24.1 to 83.6 thousand FLOPs, about three orders of magnitude fewer than FSA-Net. The FaceMesh pipeline runs at 322.9 FPS on a Tesla T4 GPU, nearly twice as fast as the fastest image-based baseline, and training completes within 28 minutes.