# Improving Global Motion Estimation in Sparse IMU-based Motion Capture with Physics

arXiv: https://arxiv.org/abs/2505.05010 · HTML: https://mlsanity.com/paper/2505.05010/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2025.

## Built on by (most cited first, 12 of 12)

- [BaroPoser: Real-time Human Motion Tracking from IMUs and Barometers in Everyday Devices](https://mlsanity.com/paper/2508.03313/) (2025)
- [Group Inertial Poser: Multi-Person Pose and Global Translation from Sparse Inertial Sensors and Ultra-Wideband Ranging](https://mlsanity.com/paper/2510.21654/) (2025)
- [Stereo-Inertial Poser: Towards Metric-Accurate Shape-Aware Motion Capture Using Sparse IMUs and a Single Stereo Camera](https://mlsanity.com/paper/2603.02130/) (2026)
- [Ground Reaction Inertial Poser: Physics-based Human Motion Capture from Sparse IMUs and Insole Pressure Sensors](https://mlsanity.com/paper/2603.16233/) (2026)
- [Seeing Without Eyes: 4D Human-Scene Understanding from Wearable IMUs](https://mlsanity.com/paper/2604.21926/) (2026)
- [EgoExoMoCap: Distributed Ego-Exo Human Motion Capture](https://mlsanity.com/paper/2607.15868/) (2026)
- [Ultra Diffusion Poser: Diffusion-Based Human Motion Tracking From Sparse Inertial Sensors and Ranging-Based Between-Sensor Distances](https://mlsanity.com/paper/2606.02153/) (2026)
- [MARIO: Motion-Augmented Real-Time Multi-Sensor Inertial Odometry](https://mlsanity.com/paper/2606.02996/) (2026)
- [IMU-HOI: A Symbiotic Framework for Coherent Human-Object Interaction and Motion Capture via Contact-Conscious Inertial Fusion](https://mlsanity.com/paper/2606.28604/) (2026)
- [EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal](https://mlsanity.com/paper/2607.13472/) (2026)
- [ArmPoser: Real-Time, Calibration-Free Arm Pose Estimation from Smartwatch IMU](https://mlsanity.com/paper/2609.08806/) (2026)
- [OmniPoser: Flexible Human Motion Recovery in the Wild with Masked Flow Matching](https://mlsanity.com/paper/2610.19857/) (2026)

## Abstract (arXiv)

By learning human motion priors, motion capture can be achieved by 6 inertial measurement units (IMUs) in recent years with the development of deep learning techniques, even though the sensor inputs are sparse and noisy. However, human global motions are still challenging to be reconstructed by IMUs. This paper aims to solve this problem by involving physics. It proposes a physical optimization scheme based on multiple contacts to enable physically plausible translation estimation in the full 3D space where the z-directional motion is usually challenging for previous works. It also considers gravity in local pose estimation which well constrains human global orientations and refines local pose estimation in a joint estimation manner. Experiments demonstrate that our method achieves more accurate motion capture for both local poses and global motions. Furthermore, by deeply integrating physics, we can also estimate 3D contact, contact forces, joint torques, and interacting proxy surfaces.