Analysis of deep learning method for 3D human posture evaluation from the perspective of transformer model

Recent advances in computer vision have established 3D human pose estimation as a critical research domain due to its significance in smart healthcare, sports biomechanics, and human-computer interaction. This technology reconstructs skeletal topology to enable high-level vision tasks such as action recognition, yet suffers from pose ambiguity caused by self-occlusion in monocular videos. To overcome this limitation, we propose a Transformer-based cross-dimensional hierarchical optimization framework addressing spatiotemporal feature modeling deficiencies in long-sequence video processing. Our approach features: 1) A dual-stage mapping mechanism where joint coordinates extracted by pretrained 2D networks are transformed through multi-head attention layers enforcing skeletal kinematic constraints to resolve spatial ambiguities; 2) An integrated four-layer real-time system (Fig 4) combining motion retargeting with early terminated DTW algorithm, accelerating pose similarity computation by 48%; 3) A dual-path optimization strategy fusing camera parameter dictionary learning with sparse-data adaptation, reducing Human3.6M annotation dependency by 30%. Experimental results demonstrate significantly enhanced occlusion robustness, establishing a new paradigm for rehabilitation training and sports biomechanics applications.