# TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors

arXiv: https://arxiv.org/abs/2105.04605 · HTML: https://mlsanity.com/paper/2105.04605/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 10 May 2021.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Monocular Total Capture: Posing Face, Body, and Hands in the Wild](https://mlsanity.com/paper/1812.01598/) (2018)
- [Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs](https://mlsanity.com/paper/1703.08014/) (2017)
- [Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data](https://mlsanity.com/paper/2003.09572/) (2020)
- [XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera](https://mlsanity.com/paper/1907.00837/) (2019)
- [Monocular Real-time Full Body Capture with Inter-part Correlations](https://mlsanity.com/paper/2012.06087/) (2020)
- [In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations](https://mlsanity.com/paper/1904.03289/) (2019)
- [Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time](https://mlsanity.com/paper/1810.04703/) (2018)
- [Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS](https://mlsanity.com/paper/2003.03972/) (2020)
- [Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach](https://mlsanity.com/paper/2003.11163/) (2020)
- [FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras](https://mlsanity.com/paper/1610.09534/) (2016)
- [Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture](https://mlsanity.com/paper/1808.01525/) (2018)
- [Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1609.03323/) (2016)

## Built on by (most cited first, 25 of 25)

- [InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactions](https://mlsanity.com/paper/2304.05684/) (2023)
- [World-Grounded Human Motion Recovery via Gravity-View Coordinates](https://mlsanity.com/paper/2409.06662/) (2024)
- [Ego-Body Pose Estimation via Ego-Head Pose Estimation](https://mlsanity.com/paper/2212.04636/) (2022)
- [Estimating Body and Hand Motion in an Ego-sensed World](https://mlsanity.com/paper/2410.03665/) (2024)
- [Differentiable Dynamics for Articulated 3d Human Motion Reconstruction](https://mlsanity.com/paper/2205.12256/) (2022)
- [LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds](https://mlsanity.com/paper/2203.14698/) (2022)
- [QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors](https://mlsanity.com/paper/2306.05666/) (2023)
- [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)
- [Fusing Monocular Images and Sparse IMU Signals for Real-time Human Motion Capture](https://mlsanity.com/paper/2309.00310/) (2023)
- [EgoCast: Forecasting Egocentric Human Pose in the Wild](https://mlsanity.com/paper/2412.02903/) (2024)
- [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)
- [SmartPoser: Arm Pose Estimation with a Smartphone and Smartwatch Using UWB and IMU Data](https://mlsanity.com/paper/2509.03451/) (2025)
- [DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization](https://mlsanity.com/paper/2406.14567/) (2024)
- [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)
- [Physical Self-Supervised Learning: IMU Sensing without Manual Labels](https://mlsanity.com/paper/2607.18361/) (2026)
- [Human Pose Driven Object Effects Recommendation](https://mlsanity.com/paper/2209.08353/) (2022)
- [Domain adaptive pose estimation via multi-level alignment](https://mlsanity.com/paper/2404.14885/) (2024)
- [SSPINNpose: A Self-Supervised PINN for Inertial Pose and Dynamics Estimation](https://mlsanity.com/paper/2506.11786/) (2025)
- [Physics-Informed Learning for Human Whole-Body Kinematics Prediction via Sparse IMUs](https://mlsanity.com/paper/2509.25704/) (2025)
- [Improving 3D Foot Motion Reconstruction in Markerless Monocular Human Motion Capture](https://mlsanity.com/paper/2603.09681/) (2026)
- [ArmPoser: Real-Time, Calibration-Free Arm Pose Estimation from Smartwatch IMU](https://mlsanity.com/paper/2609.08806/) (2026)
- [Workhorse: Learning Robust Whole-Body Humanoid Loco-Manipulation from Human Data](https://mlsanity.com/paper/2610.09117/) (2026)

## Abstract (arXiv)

Motion capture is facing some new possibilities brought by the inertial sensing technologies which do not suffer from occlusion or wide-range recordings as vision-based solutions do. However, as the recorded signals are sparse and quite noisy, online performance and global translation estimation turn out to be two key difficulties. In this paper, we present TransPose, a DNN-based approach to perform full motion capture (with both global translations and body poses) from only 6 Inertial Measurement Units (IMUs) at over 90 fps. For body pose estimation, we propose a multi-stage network that estimates leaf-to-full joint positions as intermediate results. This design makes the pose estimation much easier, and thus achieves both better accuracy and lower computation cost. For global translation estimation, we propose a supporting-foot-based method and an RNN-based method to robustly solve for the global translations with a confidence-based fusion technique. Quantitative and qualitative comparisons show that our method outperforms the state-of-the-art learning- and optimization-based methods with a large margin in both accuracy and efficiency. As a purely inertial sensor-based approach, our method is not limited by environmental settings (e.g., fixed cameras), making the capture free from common difficulties such as wide-range motion space and strong occlusion.