# Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time

arXiv: https://arxiv.org/abs/1810.04703 · HTML: https://mlsanity.com/paper/1810.04703/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2018.

## Built on by (most cited first, 30 of 47)

- [InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactions](https://mlsanity.com/paper/2304.05684/) (2023)
- [MaskedMimic: Unified Physics-Based Character Control Through Masked Motion Inpainting](https://mlsanity.com/paper/2409.14393/) (2024)
- [Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model](https://mlsanity.com/paper/2304.08577/) (2023)
- [X-Avatar: Expressive Human Avatars](https://mlsanity.com/paper/2303.04805/) (2023)
- [MIME: Human-Aware 3D Scene Generation](https://mlsanity.com/paper/2212.04360/) (2022)
- [IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds](https://mlsanity.com/paper/2304.12518/) (2023)
- [SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments](https://mlsanity.com/paper/2303.09095/) (2023)
- [LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds](https://mlsanity.com/paper/2203.14698/) (2022)
- [CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-scene Interactions](https://mlsanity.com/paper/2303.17948/) (2023)
- [HMD^2: Environment-aware Motion Generation from Single Egocentric Head-Mounted Device](https://mlsanity.com/paper/2409.13426/) (2024)
- [TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors](https://mlsanity.com/paper/2105.04605/) (2021)
- [MobilePoser: Real-Time Full-Body Pose Estimation and 3D Human Translation from IMUs in Mobile Consumer Devices](https://mlsanity.com/paper/2504.12492/) (2025)
- [Pose2Room: Understanding 3D Scenes from Human Activities](https://mlsanity.com/paper/2112.03030/) (2021)
- [Ultra Inertial Poser: Scalable Motion Capture and Tracking from Sparse Inertial Sensors and Ultra-Wideband Ranging](https://mlsanity.com/paper/2404.19541/) (2024)
- [DivaTrack: Diverse Bodies and Motions from Acceleration-Enhanced Three-Point Trackers](https://mlsanity.com/paper/2402.09211/) (2024)
- [EgoCast: Forecasting Egocentric Human Pose in the Wild](https://mlsanity.com/paper/2412.02903/) (2024)
- [Physics-based Motion Retargeting from Sparse Inputs](https://mlsanity.com/paper/2307.01938/) (2023)
- [EgoHDM: An Online Egocentric-Inertial Human Motion Capture, Localization, and Dense Mapping System](https://mlsanity.com/paper/2409.00343/) (2024)
- [SMART-Vision: Survey of Modern Action Recognition Techniques in Vision](https://mlsanity.com/paper/2501.13066/) (2025)
- [BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection](https://mlsanity.com/paper/2207.02281/) (2022)
- [MOVIN: Real-time Motion Capture using a Single LiDAR](https://mlsanity.com/paper/2309.09314/) (2023)
- [EventEgo3D++: 3D Human Motion Capture from a Head-Mounted Event Camera](https://mlsanity.com/paper/2502.07869/) (2025)
- [SUPER: Seated Upper Body Pose Estimation using mmWave Radars](https://mlsanity.com/paper/2407.02455/) (2024)
- [WheelPoser: Sparse-IMU Based Body Pose Estimation for Wheelchair Users](https://mlsanity.com/paper/2409.08494/) (2024)
- [SmartPoser: Arm Pose Estimation with a Smartphone and Smartwatch Using UWB and IMU Data](https://mlsanity.com/paper/2509.03451/) (2025)
- [SeamPose: Repurposing Seams as Capacitive Sensors in a Shirt for Upper-Body Pose Tracking](https://mlsanity.com/paper/2406.11645/) (2024)
- [DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization](https://mlsanity.com/paper/2406.14567/) (2024)
- [Suite-IN: Aggregating Motion Features from Apple Suite for Robust Inertial Navigation](https://mlsanity.com/paper/2411.07828/) (2024)
- [XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration](https://mlsanity.com/paper/2411.18377/) (2024)
- [IMUCoCo: Enabling Flexible On-Body IMU Placement for Human Pose Estimation and Activity Recognition](https://mlsanity.com/paper/2508.01894/) (2025)

## Abstract (arXiv)

We demonstrate a novel deep neural network capable of reconstructing human full body pose in real-time from 6 Inertial Measurement Units (IMUs) worn on the user's body. In doing so, we address several difficult challenges. First, the problem is severely under-constrained as multiple pose parameters produce the same IMU orientations. Second, capturing IMU data in conjunction with ground-truth poses is expensive and difficult to do in many target application scenarios (e.g., outdoors). Third, modeling temporal dependencies through non-linear optimization has proven effective in prior work but makes real-time prediction infeasible. To address this important limitation, we learn the temporal pose priors using deep learning. To learn from sufficient data, we synthesize IMU data from motion capture datasets. A bi-directional RNN architecture leverages past and future information that is available at training time. At test time, we deploy the network in a sliding window fashion, retaining real time capabilities. To evaluate our method, we recorded DIP-IMU, a dataset consisting of 10 subjects wearing 17 IMUs for validation in 64 sequences with 330,000 time instants; this constitutes the largest IMU dataset publicly available. We quantitatively evaluate our approach on multiple datasets and show results from a real-time implementation. DIP-IMU and the code are available for research purposes.