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

Lawrence Amadi¹, Gady Agam¹

¹Visual Computing Lab, Illinois Institute of Technology, Chicago, IL 60616, USA.

Sensors (Basel, Switzerland)

|December 23, 2023

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This study introduces a novel semi-supervised learning method for 3D human pose estimation. It leverages unlabeled video data to improve accuracy, significantly reducing pose estimation errors with a new posture consistency loss.

Area of Science:

Computer Vision
Machine Learning
Biomechanical Modeling

Background:

3D human pose datasets often lack variety, limiting the robustness of trained models.
Semi-supervised learning offers a way to combine limited labeled data with abundant unlabeled data.

Purpose of the Study:

To develop a novel semi-supervised framework for monocular 3D human pose estimation.
To introduce a differentiable posture consistency loss unaffected by camera orientation.
To improve pose estimation accuracy using limited labeled 3D pose data.

Main Methods:

Proposed a semi-supervised framework integrating biomechanical pose regularization.
Introduced a multi-view posture and pose consistency objective function.
Developed a novel, fully differentiable posture consistency loss.

Keywords:

human pose estimation human posture analysis semi-supervised pose estimation weakly supervised pose estimation

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Main Results:

The framework reduced Mean Per-Joint Position Error (MPJPE) by up to 15% with camera parameters.
Without camera parameters, MPJPE decreased by 17% using the posture loss.
The proposed method improved upon leading semi-supervised techniques on H36M and 3DHP datasets.

Conclusions:

The novel posture consistency loss effectively enhances monocular 3D human pose estimation.
Semi-supervised learning with the proposed framework significantly reduces pose estimation errors.
The approach offers a robust solution for training pose estimators with limited labeled data.