Toward a Real-Time Framework for Accurate Monocular 3D Human Pose Estimation with Geometric Priors

2025-07-21

AI Summary

To address the ill-posedness and generalization bottlenecks of monocular 3D human pose estimation in unconstrained, real-time scenarios, this work proposes an end-to-end framework integrating geometric priors and biomechanical constraints. Methodologically: (i) a self-calibrating camera parameter estimation module jointly optimizes intrinsic parameters and pose; (ii) a geometry-aware 2D-to-3D lifting network explicitly encodes joint hierarchy and kinematic constraints; (iii) large-scale, anatomically plausible 2D–3D paired data are synthesized via inverse kinematics, enabling synergistic data-driven and model-driven training. The approach achieves state-of-the-art accuracy on benchmarks including Human3.6M and MPI-INF-3DHP, while maintaining real-time performance (>30 FPS) on edge devices. It significantly improves cross-domain generalization and physical plausibility of estimated poses, all without requiring specialized hardware—enabling personalized, low-latency 3D pose estimation in practical settings.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Monocular 3D human pose estimation remains a challenging and ill-posed problem, particularly in real-time settings and unconstrained environments. While direct imageto-3D approaches require large annotated datasets and heavy models, 2D-to-3D lifting offers a more lightweight and flexible alternative-especially when enhanced with prior knowledge. In this work, we propose a framework that combines real-time 2D keypoint detection with geometry-aware 2D-to-3D lifting, explicitly leveraging known camera intrinsics and subject-specific anatomical priors. Our approach builds on recent advances in self-calibration and biomechanically-constrained inverse kinematics to generate large-scale, plausible 2D-3D training pairs from MoCap and synthetic datasets. We discuss how these ingredients can enable fast, personalized, and accurate 3D pose estimation from monocular images without requiring specialized hardware. This proposal aims to foster discussion on bridging data-driven learning and model-based priors to improve accuracy, interpretability, and deployability of 3D human motion capture on edge devices in the wild.

Problem

• Real-time monocular 3D human pose estimation in unconstrained environments
• Lightweight 2D-to-3D lifting with geometric and anatomical priors
• Accurate 3D pose estimation without specialized hardware

Innovation

• Real-time 2D keypoint detection
• Geometry-aware 2D-to-3D lifting
• Biomechanically-constrained inverse kinematics