research-article

Physically valid statistical models for human motion generation

Authors:

Jinxiang ChaiAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 30, Issue 3

Article No.: 19, Pages 1 - 10

https://doi.org/10.1145/1966394.1966398

Published: 19 May 2011 Publication History

Abstract

This article shows how statistical motion priors can be combined seamlessly with physical constraints for human motion modeling and generation. The key idea of the approach is to learn a nonlinear probabilistic force field function from prerecorded motion data with Gaussian processes and combine it with physical constraints in a probabilistic framework. In addition, we show how to effectively utilize the new model to generate a wide range of natural-looking motions that achieve the goals specified by users. Unlike previous statistical motion models, our model can generate physically realistic animations that react to external forces or changes in physical quantities of human bodies and interaction environments. We have evaluated the performance of our system by comparing against ground-truth motion data and alternative methods.

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Cited By

Yue JLi BPetrré JSeyfried AWang H(2024)Human Motion Prediction Under Unexpected Perturbation2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00149(1501-1511)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00149
Avogaro ACunico FRosenhahn BSetti F(2023)Markerless human pose estimation for biomedical applications: a surveyFrontiers in Computer Science10.3389/fcomp.2023.11531605Online publication date: 6-Jul-2023
https://doi.org/10.3389/fcomp.2023.1153160
Tao HHou SZou CBao HXu W(2023)Neural Motion GraphSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618181(1-11)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.1145/3610548.3618181
Show More Cited By

Index Terms

Physically valid statistical models for human motion generation
1. Computing methodologies
  1. Computer graphics
    1. Animation
    2. Graphics systems and interfaces
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Graphics input devices

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 30, Issue 3

May 2011

127 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1966394

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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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 May 2011

Accepted: 01 February 2011

Revised: 01 January 2011

Received: 01 November 2010

Published in TOG Volume 30, Issue 3

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Cited By

Yue JLi BPetrré JSeyfried AWang H(2024)Human Motion Prediction Under Unexpected Perturbation2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00149(1501-1511)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00149
Avogaro ACunico FRosenhahn BSetti F(2023)Markerless human pose estimation for biomedical applications: a surveyFrontiers in Computer Science10.3389/fcomp.2023.11531605Online publication date: 6-Jul-2023
https://doi.org/10.3389/fcomp.2023.1153160
Tao HHou SZou CBao HXu W(2023)Neural Motion GraphSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618181(1-11)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.1145/3610548.3618181
Hwang JIshii S(2023)Simplified Physical Model‐based Balance‐preserving Motion Re‐targeting for Physical SimulationComputer Graphics Forum10.1111/cgf.1499643:1Online publication date: 11-Dec-2023
https://doi.org/10.1111/cgf.14996
Dong MXu C(2023)Skeleton-Based Human Motion Prediction With Privileged SupervisionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.316686134:12(10419-10432)Online publication date: Dec-2023
https://doi.org/10.1109/TNNLS.2022.3166861
Li PAberman KZhang ZHanocka RSorkine-Hornung O(2022)GANimatorACM Transactions on Graphics10.1145/3528223.353015741:4(1-12)Online publication date: 22-Jul-2022
https://dl.acm.org/doi/10.1145/3528223.3530157
Balreira DDe Figueiredo LVolkweis MWalter M(2022)A texture-driven model for male baldness2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI55357.2022.9991744(162-167)Online publication date: 24-Oct-2022
https://doi.org/10.1109/SIBGRAPI55357.2022.9991744
Zhang ZZhu YRai RDoermann D(2022)PIMNet: Physics-Infused Neural Network for Human Motion PredictionIEEE Robotics and Automation Letters10.1109/LRA.2022.31888927:4(8949-8955)Online publication date: Oct-2022
https://doi.org/10.1109/LRA.2022.3188892
Garcia-Barrientos ABalderas-Navarro RMacias-Velasquez SHoyo-Montano JGarcia-Ramirez MEspejel-Blanco DPlaza-Castillo J(2022)Gait Analysis Using the Physics Toolbox AppIEEE Access10.1109/ACCESS.2022.315831510(31732-31739)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3158315
Feng XLiu JWang HYang YBao HBickel BXu W(2021)Computational Design of Skinned Quad-RobotsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.295721827:6(2881-2895)Online publication date: 1-Jun-2021
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