Reliability-Gated Fusion of Consumer Head and Foot IMUs for Lower-Body 3D Pose

arXiv:2609.35764 · cs.CV, cs.HC · Submitted 2026-09-28 · Read on arXiv

cs.CV, cs.HC

Submitted: 2026-09-28

Updated: 2026-09-28

Comments: 10 pages, 4 figures, 3 tables. Code: https://github.com/ZhilinGuo/reliability-gated-imu-fusion

Code: https://github.com/ZhilinGuo/reliability-gated-imu-fusion

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Calibration — 17× in this paper · explained in 7 episodes
• Alignment — 14× in this paper · explained in 1 episode
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Ablation — 6× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• European Conference on Computer Vision — 2× in this paper
• Failure Modes — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Digital Twin — 1× in this paper · explained in 4 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Winter Conference on Applications of Computer Vision — 1× in this paper
• Zero-Shot Transfer — 1× in this paper · explained in 3 episodes

Sources

• Towards Real-World Wearable Motion Reconstruction
• HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observations
• Ground Reaction Inertial Poser: Physics-based Human Motion Capture from Sparse IMUs and Insole Pressure Sensors
• EgoPoser: Robust Real-Time Egocentric Pose Estimation from Sparse and Intermittent Observations Everywhere
• IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds
• Estimating Lower Body Kinematics using a Lie Group Constrained Extended Kalman Filter and Reduced IMU Count
• MobilePoser: Real-Time Full-Body Pose Estimation and 3D Human Translation from IMUs in Mobile Consumer Devices
• DivaTrack: Diverse Bodies and Motions from Acceleration-Enhanced Three-Point Trackers
• SAM 3D Body: Robust Full-Body Human Mesh Recovery
• Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors
• Progressive Inertial Poser: Progressive Real-Time Kinematic Chain Estimation for 3D Full-Body Pose from Three IMU Sensors
• Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture

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