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Realtime human motion control with a small number of inertial sensors

Authors:

Taehyun RheeAuthors Info & Claims

I3D '11: Symposium on Interactive 3D Graphics and Games

Pages 133 - 140

https://doi.org/10.1145/1944745.1944768

Published: 18 February 2011 Publication History

Abstract

This paper introduces an approach to performance animation that employs a small number of motion sensors to create an easy-to-use system for an interactive control of a full-body human character. Our key idea is to construct a series of online local dynamic models from a prerecorded motion database and utilize them to construct full-body human motion in a maximum a posteriori framework (MAP). We have demonstrated the effectiveness of our system by controlling a variety of human actions, such as boxing, golf swinging, and table tennis, in real time. Given an appropriate motion capture database, the results are comparable in quality to those obtained from a commercial motion capture system with a full set of motion sensors (e.g., XSens [2009]); however, our performance animation system is far less intrusive and expensive because it requires a small of motion sensors for full body control. We have also evaluated the performance of our system by leave-one-out-experiments and by comparing with two baseline algorithms.

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Cited By

Kim DYeo HPark K(2025)Effects of an Avatar Control on VR EmbodimentBioengineering10.3390/bioengineering1201003212:1(32)Online publication date: 3-Jan-2025
https://doi.org/10.3390/bioengineering12010032
Armani RQian CJiang JHolz C(2024)Ultra Inertial Poser: Scalable Motion Capture and Tracking from Sparse Inertial Sensors and Ultra-Wideband RangingACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657465(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657465
Kim DKoo JHwang JSeo MJung IPark K(2024)Full-body Avatar Generation for Increased Embodiment2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)10.1109/VRW62533.2024.00328(1063-1065)Online publication date: 16-Mar-2024
https://doi.org/10.1109/VRW62533.2024.00328
Show More Cited By

Index Terms

Realtime human motion control with a small number of inertial sensors
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        Motion capture
  2. Computer graphics
    1. Animation
      1. Motion capture
      2. Motion processing
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Haptic devices
      2. Touch screens

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Published In

cover image ACM Conferences

I3D '11: Symposium on Interactive 3D Graphics and Games

February 2011

207 pages

ISBN:9781450305655

DOI:10.1145/1944745

General Chairs:
Michael Garland
NVIDIA Research
,
Rui Wang
University of Massachusetts

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 February 2011

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I3D '11

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SIGGRAPH

I3D '11: Symposium on Interactive 3D Graphics and Games

February 18 - 20, 2011

California, San Francisco

Acceptance Rates

I3D '11 Paper Acceptance Rate 24 of 64 submissions, 38%;

Overall Acceptance Rate 148 of 485 submissions, 31%

Upcoming Conference

I3D '25

Sponsor:
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Symposium on Interactive 3D Graphics and Games

May 7 - 9, 2025

Jersey City , NJ , USA

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Cited By

Kim DYeo HPark K(2025)Effects of an Avatar Control on VR EmbodimentBioengineering10.3390/bioengineering1201003212:1(32)Online publication date: 3-Jan-2025
https://doi.org/10.3390/bioengineering12010032
Armani RQian CJiang JHolz C(2024)Ultra Inertial Poser: Scalable Motion Capture and Tracking from Sparse Inertial Sensors and Ultra-Wideband RangingACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657465(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657465
Kim DKoo JHwang JSeo MJung IPark K(2024)Full-body Avatar Generation for Increased Embodiment2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)10.1109/VRW62533.2024.00328(1063-1065)Online publication date: 16-Mar-2024
https://doi.org/10.1109/VRW62533.2024.00328
Zeng QZheng GLiu Q(2024)DTP: learning to estimate full-body pose in real-time from sparse VR sensor measurementsVirtual Reality10.1007/s10055-024-01011-128:2Online publication date: 23-May-2024
https://dl.acm.org/doi/10.1007/s10055-024-01011-1
Lee SStarke SYe YWon JWinkler A(2023)QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse SensorsACM SIGGRAPH 2023 Conference Proceedings10.1145/3588432.3591504(1-9)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1145/3588432.3591504
DeVrio NMollyn VHarrison C(2023)SmartPoser: Arm Pose Estimation with a Smartphone and Smartwatch Using UWB and IMU DataProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology10.1145/3586183.3606821(1-11)Online publication date: 29-Oct-2023
https://dl.acm.org/doi/10.1145/3586183.3606821
Milef NSueda SKalantari N(2023)Variational Pose Prediction with Dynamic Sample Selection from Sparse Tracking SignalsComputer Graphics Forum10.1111/cgf.1476742:2(359-369)Online publication date: 23-May-2023
https://doi.org/10.1111/cgf.14767
Ye YLiu LHu LXia S(2023)Neural3Points: Learning to Generate Physically Realistic Full‐body Motion for Virtual Reality UsersComputer Graphics Forum10.1111/cgf.1463441:8(183-194)Online publication date: 20-Mar-2023
https://doi.org/10.1111/cgf.14634
Kaufmann MSong JGuo CShen KJiang TTang CZárate JHilliges O(2023)EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.01345(14586-14597)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.01345
Anvari TPark K(2023)Geometry-Incorporated Posing of a Full-Body Avatar From Sparse TrackersIEEE Access10.1109/ACCESS.2023.329932311(78858-78866)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3299323
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