# Physical Non-inertial Poser (PNP): Modeling Non-inertial Effects in Sparse-inertial Human Motion Capture

arXiv: https://arxiv.org/abs/2404.19619 · HTML: https://mlsanity.com/paper/2404.19619/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2024.

## Built on by (most cited first, 24 of 24)

- [Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture](https://mlsanity.com/paper/2506.10580/) (2025)
- [UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units](https://mlsanity.com/paper/2505.09393/) (2025)
- [Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models](https://mlsanity.com/paper/2506.15290/) (2025)
- [Motion Capture from Inertial and Vision Sensors](https://mlsanity.com/paper/2407.16341/) (2024)
- [Mojito: LLM-Aided Motion Instructor with Jitter-Reduced Inertial Tokens](https://mlsanity.com/paper/2502.16175/) (2025)
- [IMUCoCo: Enabling Flexible On-Body IMU Placement for Human Pose Estimation and Activity Recognition](https://mlsanity.com/paper/2508.01894/) (2025)
- [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)
- [ASMR: Adaptive Skeleton-Mesh Rigging and Skinning via 2D Generative Prior](https://mlsanity.com/paper/2503.13579/) (2025)
- [MagShield: Towards Better Robustness in Sparse Inertial Motion Capture Under Magnetic Disturbances](https://mlsanity.com/paper/2506.22907/) (2025)
- [DiffCap: Diffusion-based Real-time Human Motion Capture using Sparse IMUs and a Monocular Camera](https://mlsanity.com/paper/2508.06139/) (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)
- [Shape-aware Inertial Poser: Motion Tracking for Humans with Diverse Shapes Using Sparse Inertial Sensors](https://mlsanity.com/paper/2510.17101/) (2025)
- [EgoExoMoCap: Distributed Ego-Exo Human Motion Capture](https://mlsanity.com/paper/2607.15868/) (2026)
- [Step2Motion: Locomotion Reconstruction from Pressure Sensing Insoles](https://mlsanity.com/paper/2510.22712/) (2025)
- [OmniEgoCap: Camera-Agnostic Sequence-Level Egocentric Motion Reconstruction](https://mlsanity.com/paper/2512.19283/) (2025)
- [Garment Inertial Denoiser (GID): Endowing Accurate Motion Capture via Loose IMU Denoiser](https://mlsanity.com/paper/2601.01360/) (2026)
- [DexterCap: An Affordable and Automated System for Capturing Dexterous Hand-Object Manipulation](https://mlsanity.com/paper/2601.05844/) (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)
- [ArmPoser: Real-Time, Calibration-Free Arm Pose Estimation from Smartwatch IMU](https://mlsanity.com/paper/2609.08806/) (2026)

## Abstract (arXiv)

Existing inertial motion capture techniques use the human root coordinate frame to estimate local poses and treat it as an inertial frame by default. We argue that when the root has linear acceleration or rotation, the root frame should be considered non-inertial theoretically. In this paper, we model the fictitious forces that are non-neglectable in a non-inertial frame by an auto-regressive estimator delicately designed following physics. With the fictitious forces, the force-related IMU measurement (accelerations) can be correctly compensated in the non-inertial frame and thus Newton's laws of motion are satisfied. In this case, the relationship between the accelerations and body motions is deterministic and learnable, and we train a neural network to model it for better motion capture. Furthermore, to train the neural network with synthetic data, we develop an IMU synthesis by simulation strategy to better model the noise model of IMU hardware and allow parameter tuning to fit different hardware. This strategy not only establishes the network training with synthetic data but also enables calibration error modeling to handle bad motion capture calibration, increasing the robustness of the system. Code is available at https://xinyu-yi.github.io/PNP/.