mlsanityData to 14 Oct 2026

One Sensor, Whole Body - 3D Body Pose from a Single Consumer Earbud IMU

arXiv 2609.34978 ↗First posted 28 Sep 2026cs.CV cs.HCcited by 0

Not on the map. Why?

Abstract (arXiv)

Consumer earbuds already stream inertial motion data from the head, one of the most widely worn sensor locations on the body. We ask how much of the 3D body pose a single such head IMU can recover, and whether adding more consumer sensors actually helps. We build a multimodal capture pipeline that records four-view RGB-D video together with an AirPods head IMU and two Striv insole IMUs, synchronize the streams post-hoc, and generate pseudo-ground-truth with SAM 3D Body, yielding a 35-take single-subject benchmark spanning gait, turning, vertical, everyday, and clinically inspired motions. Adapting two recurrent model families (IMUPoser and MobilePoser), we show that one head IMU recovers lower-body pose at 79.0 mm rigid-MPJPE and per-foot ground contact at 0.809 macro-F1, and that a causal variant retains most of this accuracy at streaming latency. In paired per-take significance tests across both families, adding the consumer foot IMUs never significantly improves pose and significantly degrades it in two of four model-split combinations; a mounting-bias probe and feet-only ablation identify insole orientation quality, not foot placement, as the mechanism. Extending the output to a 20-joint full-body skeleton maps the boundary: gross distal-arm motion is partially recoverable from the head alone, proximal upper-body pose is not, and staged fine-tuning recovers the leg accuracy that naive joint training sacrifices to multi-task dilution. For learned pose from consumer wearables, sensor reliability, not sensor count, is the binding constraint here. For the devices tested, the earbud is its sweet spot. Code is available at https://github.com/ZhilinGuo/one-sensor-whole-body.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 25 references that are in the map. First posted on arXiv 28 Sep 2026.

Builds on

15 of 25 references.

References · 15

  1. 2019AMASS: Archive of Motion Capture as Surface Shapescited by 1,236
  2. 2022Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensorscited by 146
  3. 2023Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Modelcited by 107
  4. 2022Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generationcited by 76
  5. 2026SAM 3D Body: Robust Full-Body Human Mesh Recoverycited by 69
  6. 2023IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbudscited by 54
  7. 2023EgoPoser: Robust Real-Time Egocentric Pose Estimation from Sparse and Intermittent Observations Everywherecited by 34
  8. 2023DiffusionPoser: Real-time Human Motion Reconstruction From Arbitrary Sparse Sensors Using Autoregressive Diffusioncited by 31
  9. 2023Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensorscited by 30
  10. 2024HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observationscited by 25
  11. 2025MobilePoser: Real-Time Full-Body Pose Estimation and 3D Human Translation from IMUs in Mobile Consumer Devicescited by 23
  12. 2019DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Imagecited by 14
3 more
  1. 2024DivaTrack: Diverse Bodies and Motions from Acceleration-Enhanced Three-Point Trackerscited by 11
  2. 2025Progressive Inertial Poser: Progressive Real-Time Kinematic Chain Estimation for 3D Full-Body Pose from Three IMU Sensorscited by 4
  3. 2021Estimating Lower Body Kinematics using a Lie Group Constrained Extended Kalman Filter and Reduced IMU Countcited by 2

Built on by

Not cited yet.