Enhancement of human 3D pose estimation using a novel concept of depth prediction with pose alignment from a single 2D image

Abstract

2D poses are insufficient to estimate human activities under the geometric ambiguities, occlusion, diverse appearance, and viewpoints inside the image. As a result, 3D posing has emerged as an attractive research area in recent decades due to its ability to distinguish human activities effectively. Human skeletal 3D posture is the foundation for 3D avatar creation, the construction of human 3D meshes, recognition of human actions or activities, augmented reality, etc. Therefore, estimating the correct human 3D skeleton or pose with minimum errors is an essential task. This research paper proposes a depth predictor module; an innovative and creative approach of depth prediction for evaluating the human 3D pose from a single 2D image. However, no predicted 3D pose is entirely free from errors. Therefore, it needs to reduce the inaccuracy after predicting the 3D pose. For this purpose, an enhancement approach, pose alignment, is used to reduce the positioning inaccuracies of the anticipated 3D pose. The specialty of the proposed model is that it does not require any training dataset, and it effectively estimates 3D poses under any scale variations. This proposed method is evaluated on different datasets: Human3.6M, HumanEva-I, NTU RGB + D, MPII, UAV-human and the Articulated Free Fall dataset. In terms of errors, the proposed approach achieves the minimum 3D pose error compared to the existing 3D pose estimation techniques. The proposed depth prediction approach reduces the average MPJPE value to 48.30, and the pose alignment module further reduces it to 40.61. In addition, the proposed model takes an inference time of 1.806 s per frame for a single person.