PosturePose: Optimized Posture Analysis for Semi-Supervised Monocular 3D Human Pose Estimation

Abstract

One motivation for studying semi-supervised techniques for human pose estimation is to compensate for the lack of variety in curated 3D human pose datasets by combining labeled 3D pose data with readily available unlabeled video data—effectively, leveraging the annotations of the former and the rich variety of the latter to train more robust pose estimators. In this paper, we propose a novel, fully differentiable posture consistency loss that is unaffected by camera orientation and improves monocular human pose estimators trained with limited labeled 3D pose data. Our semi-supervised monocular 3D pose framework combines biomechanical pose regularization with a multi-view posture (and pose) consistency objective function. We show that posture optimization was effective at decreasing pose estimation errors when applied to a 2D–3D lifting network (VPose3D) and two well-studied datasets (H36M and 3DHP). Specifically, the proposed semi-supervised framework with multi-view posture and pose loss lowered the mean per-joint position error (MPJPE) of leading semi-supervised methods by up to 15% (−7.6 mm) when camera parameters of unlabeled poses were provided. Without camera parameters, our semi-supervised framework with posture loss improved semi-supervised state-of-the-art methods by 17% (−15.6 mm decrease in MPJPE). Overall, our pose models compete favorably with other high-performing pose models trained under similar conditions with limited labeled data.

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

“…This adaptive function allows layers with more channels to engage in more inter-channel interactions. The specific adaptive function is described in Formula (10).…”

“…Through comparison with standard poses, the study demonstrated the algorithm's ability to accurately recognize various badminton action poses, with a recognition rate of up to 94%. Amadi et al [10] (2023) introduced a novel and fully differentiable pose consistency loss method. This method is unaffected by camera direction and has shown improvements in single-view human pose estimators trained using limited labeled 3D pose data.…”