MI-Poser

Author:

Arakawa Riku1ORCID,Zhou Bing2ORCID,Krishnan Gurunandan2ORCID,Goel Mayank1ORCID,Nayar Shree K.2ORCID

Affiliation:

1. Carnegie Mellon University, Pittsburgh, United States

2. Snap Research, New York, United States

Abstract

Inside-out tracking of human body poses using wearable sensors holds significant potential for AR/VR applications, such as remote communication through 3D avatars with expressive body language. Current inside-out systems often rely on vision-based methods utilizing handheld controllers or incorporating densely distributed body-worn IMU sensors. The former limits hands-free and occlusion-robust interactions, while the latter is plagued by inadequate accuracy and jittering. We introduce a novel body tracking system, MI-Poser, which employs AR glasses and two wrist-worn electromagnetic field (EMF) sensors to achieve high-fidelity upper-body pose estimation while mitigating metal interference. Our lightweight system demonstrates a minimal error (6.6 cm mean joint position error) with real-world data collected from 10 participants. It remains robust against various upper-body movements and operates efficiently at 60 Hz. Furthermore, by incorporating an IMU sensor co-located with the EMF sensor, MI-Poser presents solutions to counteract the effects of metal interference, which inherently disrupts the EMF signal during tracking. Our evaluation effectively showcases the successful detection and correction of interference using our EMF-IMU fusion approach across environments with diverse metal profiles. Ultimately, MI-Poser offers a practical pose tracking system, particularly suited for body-centric AR applications.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction

Reference71 articles.

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2. MeCap: Whole-Body Digitization for Low-Cost VR/AR Headsets

3. Karan Ahuja , Sven Mayer , Mayank Goel , and Chris Harrison . 2021 . Pose-on-the-Go: Approximating User Pose with Smartphone Sensor Fusion and Inverse Kinematics. In CHI '21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama , Japan , May 8-13, 2021. ACM, New York, 9:1--9:12. https://doi.org/10.1145/3411764.3445582 10.1145/3411764.3445582 Karan Ahuja, Sven Mayer, Mayank Goel, and Chris Harrison. 2021. Pose-on-the-Go: Approximating User Pose with Smartphone Sensor Fusion and Inverse Kinematics. In CHI '21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021. ACM, New York, 9:1--9:12. https://doi.org/10.1145/3411764.3445582

4. Karan Ahuja , Vivian Shen , Cathy Mengying Fang , Nathan Riopelle , Andy Kong , and Chris Harrison . 2022 . ControllerPose: Inside-Out Body Capture with VR Controller Cameras. In CHI '22: CHI Conference on Human Factors in Computing Systems , New Orleans, LA, USA , 29 April 2022 - 5 May 2022. ACM, New York, 108:1--108:13. https://doi.org/10.1145/3491102.3502105 10.1145/3491102.3502105 Karan Ahuja, Vivian Shen, Cathy Mengying Fang, Nathan Riopelle, Andy Kong, and Chris Harrison. 2022. ControllerPose: Inside-Out Body Capture with VR Controller Cameras. In CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022 - 5 May 2022. ACM, New York, 108:1--108:13. https://doi.org/10.1145/3491102.3502105

5. Sadegh Aliakbarian , Pashmina Cameron , Federica Bogo , Andrew W. Fitzgibbon , and Thomas J . Cashman . 2022 . FLAG : Flow-based 3D Avatar Generation from Sparse Observations. CoRR abs/2203.05789 (2022). https://doi.org/10.48550/arXiv.2203.05789 10.48550/arXiv.2203.05789 Sadegh Aliakbarian, Pashmina Cameron, Federica Bogo, Andrew W. Fitzgibbon, and Thomas J. Cashman. 2022. FLAG: Flow-based 3D Avatar Generation from Sparse Observations. CoRR abs/2203.05789 (2022). https://doi.org/10.48550/arXiv.2203.05789

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