# Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

arXiv: https://arxiv.org/abs/1703.08014 · HTML: https://mlsanity.com/paper/1703.08014/

## Key facts

- Cited by 109 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2017.

## Built on by (most cited first, 30 of 109)

- [InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactions](https://mlsanity.com/paper/2304.05684/) (2023)
- [Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors](https://mlsanity.com/paper/2103.17265/) (2021)
- [Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild](https://mlsanity.com/paper/2406.09905/) (2024)
- [AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing](https://mlsanity.com/paper/2207.13784/) (2022)
- [EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild](https://mlsanity.com/paper/2308.16894/) (2023)
- [Ego-Body Pose Estimation via Ego-Head Pose Estimation](https://mlsanity.com/paper/2212.04636/) (2022)
- [QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars](https://mlsanity.com/paper/2209.09391/) (2022)
- [Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation](https://mlsanity.com/paper/2203.15720/) (2022)
- [Neural Monocular 3D Human Motion Capture with Physical Awareness](https://mlsanity.com/paper/2105.01057/) (2021)
- [IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds](https://mlsanity.com/paper/2304.12518/) (2023)
- [EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors](https://mlsanity.com/paper/2305.01599/) (2023)
- [SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments](https://mlsanity.com/paper/2303.09095/) (2023)
- [LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors](https://mlsanity.com/paper/2205.15410/) (2022)
- [I'M HOI: Inertia-aware Monocular Capture of 3D Human-Object Interactions](https://mlsanity.com/paper/2312.08869/) (2023)
- [Realistic Full-Body Tracking from Sparse Observations via Joint-Level Modeling](https://mlsanity.com/paper/2308.08855/) (2023)
- [HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR](https://mlsanity.com/paper/2203.09215/) (2022)
- [mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors](https://mlsanity.com/paper/2210.08394/) (2022)
- [EgoPoser: Robust Real-Time Egocentric Pose Estimation from Sparse and Intermittent Observations Everywhere](https://mlsanity.com/paper/2308.06493/) (2023)
- [DiffusionPoser: Real-time Human Motion Reconstruction From Arbitrary Sparse Sensors Using Autoregressive Diffusion](https://mlsanity.com/paper/2308.16682/) (2023)
- [Mocap Everyone Everywhere: Lightweight Motion Capture With Smartwatches and a Head-Mounted Camera](https://mlsanity.com/paper/2401.00847/) (2024)
- [Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors](https://mlsanity.com/paper/2312.02196/) (2023)
- [QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors](https://mlsanity.com/paper/2306.05666/) (2023)
- [Egocentric Pose Estimation from Human Vision Span](https://mlsanity.com/paper/2104.05167/) (2021)
- [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)
- [HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observations](https://mlsanity.com/paper/2403.03561/) (2024)
- [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)
- [SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data](https://mlsanity.com/paper/2311.02191/) (2023)

## Abstract (arXiv)

We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under-constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables 3D human pose estimation using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall.