ENHANCING 3D HUMAN POSE ESTIMATION THROUGH MULTI-FEATURE FUSION

3D human pose estimation (3D-HPE) has emerged as a prominent research area with diverse applications. This work focuses on enhancing the accuracy of 3D-HPE by proposing a two-stage model with a multi-feature fusion approach. The proposed model utilizes convolutional kernels of different sizes to extract feature maps with diverse resolutions and dimensions. These feature maps, along with the 2D coordinates of key joint points from the input frame, are fused in the first stage. In the second stage, the fused feature map is combined with the feature points of 2D key joints to jointly predict the key joints in 3D space. Experimental evaluations demonstrate the superiority of the proposed model over representative methods. It achieves significant improvements of 9.47% and 8.55% in average MPJPE and average P-MPJPE, respectively, which are critical metrics for evaluating pose estimation accuracy. The proposed two-stage model with multi-feature fusion offers a comprehensive and accurate approach to 3D-HPE. It outperforms existing methods and showcases its effectiveness in capturing the intricate details of human poses. The results validate the significance of the proposed model in advancing the field of 3D-HPE