Toward a Real-Time Framework for Accurate Monocular 3D Human Pose Estimation with Geometric Priors
This work addresses the challenge of accurate and fast 3D human pose estimation from monocular images for applications in real-world settings, representing an incremental improvement by integrating existing methods with geometric priors.
The paper tackles the problem of real-time monocular 3D human pose estimation in unconstrained environments by proposing a framework that combines 2D keypoint detection with geometry-aware 2D-to-3D lifting, leveraging camera intrinsics and anatomical priors to improve accuracy and deployability on edge devices.
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.