Wearable Inertial Measurement Units (IMUs) allow nonintrusive motion tracking, but limited sensor placements can introduce uncertainty in capturing detailed full-body
movements. We introduce the Probabilistic Inertial Poser (ProbIP), a novel probabilistic
model that transforms sparse IMU data into human motion predictions without physical constraints. ProbIP utilizes RU-Mamba blocks to predict a matrix Fisher distribution over rotations, effectively estimating both rotation matrices and associated uncertainties. To refine motion distribution through layers, our Progressive Distribution Narrowing (PDN) technique enables stable learning across a diverse range of motions. Experimental results demonstrate that ProbIP achieves state-of-the-art performance on multiple public datasets with six and fewer IMU sensors.
Related publications
1. M Kim, Y Jeon, S Jo, Probabilistic Inertial Poser (ProbIP): Uncertainty-aware Human Motion Modeling from Sparse Inertial Sensors, International Conference on Computer Vision (ICCV 2025), [LINK] [PDF] [VIDEO]
