Article

Simulating biped behaviors from human motion data

Authors:

Jehee LeeAuthors Info & Claims

SIGGRAPH '07: ACM SIGGRAPH 2007 papers

Pages 107 - es

https://doi.org/10.1145/1275808.1276511

Published: 29 July 2007 Publication History

Abstract

Physically based simulation of human motions is an important issue in the context of computer animation, robotics and biomechanics. We present a new technique for allowing our physically-simulated planar biped characters to imitate human behaviors. Our contribution is twofold. We developed an optimization method that transforms any (either motion-captured or kinematically synthesized) biped motion into a physically-feasible, balance-maintaining simulated motion. Our optimization method allows us to collect a rich set of training data that contains stylistic, personality-rich human behaviors. Our controller learning algorithm facilitates the creation and composition of robust dynamic controllers that are learned from training data. We demonstrate a planar articulated character that is dynamically simulated in real time, equipped with an integrated repertoire of motor skills, and controlled interactively to perform desired motions.

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Cited By

Li JHuang TZhu QWong T(2024)Physics-based Scene Layout Generation from Human MotionACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657517(1-10)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657517
Su MHuang Y(2023)Multi-Speed Walking Gait Generation for Bipedal Robots Based on Reinforcement Learning and Human Motion Imitation2023 42nd Chinese Control Conference (CCC)10.23919/CCC58697.2023.10240767(4815-4821)Online publication date: 24-Jul-2023
https://doi.org/10.23919/CCC58697.2023.10240767
Zhu QZhang HLan MHan L(2023)Neural Categorical Priors for Physics-Based Character ControlACM Transactions on Graphics10.1145/361839742:6(1-16)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3618397
Show More Cited By

Index Terms

Simulating biped behaviors from human motion data
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Virtual reality

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cover image ACM Conferences

SIGGRAPH '07: ACM SIGGRAPH 2007 papers

August 2007

1019 pages

ISBN:9781450378369

DOI:10.1145/1275808

Conference Chair:
Marc Levoy

Copyright © 2007 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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Published: 29 July 2007

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SIGGRAPH07: Special Interest Group on Computer Graphics and Interactive Techniques Conference

August 5 - 9, 2007

California, San Diego

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SIGGRAPH '07 Paper Acceptance Rate 108 of 455 submissions, 24%;

Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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Cited By

Li JHuang TZhu QWong T(2024)Physics-based Scene Layout Generation from Human MotionACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657517(1-10)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657517
Su MHuang Y(2023)Multi-Speed Walking Gait Generation for Bipedal Robots Based on Reinforcement Learning and Human Motion Imitation2023 42nd Chinese Control Conference (CCC)10.23919/CCC58697.2023.10240767(4815-4821)Online publication date: 24-Jul-2023
https://doi.org/10.23919/CCC58697.2023.10240767
Zhu QZhang HLan MHan L(2023)Neural Categorical Priors for Physics-Based Character ControlACM Transactions on Graphics10.1145/361839742:6(1-16)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3618397
Feng YXu XLiu L(2023)MuscleVAE: Model-Based Controllers of Muscle-Actuated CharactersSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618137(1-11)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.1145/3610548.3618137
Xu PShang XZordan VKaramouzas I(2023)Composite Motion Learning with Task ControlACM Transactions on Graphics10.1145/359244742:4(1-16)Online publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1145/3592447
Cao JGao JZuo WLiu JXin XJin M(2023)The Tumbling Motion Planning of Humanoid Robot with Rolling-Stone Dynamics Model2022 IEEE International Conference on Cyborg and Bionic Systems (CBS)10.1109/CBS55922.2023.10115304(222-227)Online publication date: 24-Mar-2023
https://doi.org/10.1109/CBS55922.2023.10115304
Park JMin SChang PLee JPark MLee J(2022)Generative GaitNetACM SIGGRAPH 2022 Conference Proceedings10.1145/3528233.3530717(1-9)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1145/3528233.3530717
Hamalainen PToikka JBabadi ALiu C(2022)Visualizing Movement Control Optimization LandscapesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.301818728:3(1648-1660)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TVCG.2020.3018187
Huang BPan LYang YJu JWang Y(2022)Neural MoCon: Neural Motion Control for Physically Plausible Human Motion Capture2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52688.2022.00631(6407-6416)Online publication date: Jun-2022
https://doi.org/10.1109/CVPR52688.2022.00631
Denizdurduran BMarkram HGewaltig M(2022)Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learningBiological Cybernetics10.1007/s00422-022-00940-x116:5-6(711-726)Online publication date: 11-Aug-2022
https://doi.org/10.1007/s00422-022-00940-x
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