{"ID":2850771,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.21654","arxiv_id":"2510.21654","title":"Group Inertial Poser: Multi-Person Pose and Global Translation from Sparse Inertial Sensors and Ultra-Wideband Ranging","abstract":"Tracking human full-body motion using sparse wearable inertial measurement units (IMUs) overcomes the limitations of occlusion and instrumentation of the environment inherent in vision-based approaches. However, purely IMU-based tracking compromises translation estimates and accurate relative positioning between individuals, as inertial cues are inherently self-referential and provide no direct spatial reference for others. In this paper, we present a novel approach for robustly estimating body poses and global translation for multiple individuals by leveraging the distances between sparse wearable sensors - both on each individual and across multiple individuals. Our method Group Inertial Poser estimates these absolute distances between pairs of sensors from ultra-wideband ranging (UWB) and fuses them with inertial observations as input into structured state-space models to integrate temporal motion patterns for precise 3D pose estimation. Our novel two-step optimization further leverages the estimated distances for accurately tracking people's global trajectories through the world. We also introduce GIP-DB, the first IMU+UWB dataset for two-person tracking, which comprises 200 minutes of motion recordings from 14 participants. In our evaluation, Group Inertial Poser outperforms previous state-of-the-art methods in accuracy and robustness across synthetic and real-world data, showing the promise of IMU+UWB-based multi-human motion capture in the wild. Code, models, dataset: https://github.com/eth-siplab/GroupInertialPoser","short_abstract":"Tracking human full-body motion using sparse wearable inertial measurement units (IMUs) overcomes the limitations of occlusion and instrumentation of the environment inherent in vision-based approaches. However, purely IMU-based tracking compromises translation estimates and accurate relative positioning between indivi...","url_abs":"https://arxiv.org/abs/2510.21654","url_pdf":"https://arxiv.org/pdf/2510.21654v1","authors":"[\"Ying Xue\",\"Jiaxi Jiang\",\"Rayan Armani\",\"Dominik Hollidt\",\"Yi-Chi Liao\",\"Christian Holz\"]","published":"2025-10-24T17:11:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.GR\",\"cs.HC\"]","methods":"[]","has_code":false,"code_links":[{"ID":607839,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2850771,"paper_url":"https://arxiv.org/abs/2510.21654","paper_title":"Group Inertial Poser: Multi-Person Pose and Global Translation from Sparse Inertial Sensors and Ultra-Wideband Ranging","repo_url":"https://github.com/eth-siplab/GroupInertialPoser","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}