Article

Automated extraction and parameterization of motions in large data sets

Authors:

Michael GleicherAuthors Info & Claims

SIGGRAPH '04: ACM SIGGRAPH 2004 Papers

Pages 559 - 568

https://doi.org/10.1145/1186562.1015760

Published: 01 August 2004 Publication History

Abstract

Large motion data sets often contain many variants of the same kind of motion, but without appropriate tools it is difficult to fully exploit this fact. This paper provides automated methods for identifying logically similar motions in a data set and using them to build a continuous and intuitively parameterized space of motions. To find logically similar motions that are numerically dissimilar, our search method employs a novel distance metric to find "close" motions and then uses them as intermediaries to find more distant motions. Search queries are answered at interactive speeds through a precomputation that compactly represents all possibly similar motion segments. Once a set of related motions has been extracted, we automatically register them and apply blending techniques to create a continuous space of motions. Given a function that defines relevant motion parameters, we present a method for extracting motions from this space that accurately possess new parameters requested by the user. Our algorithm extends previous work by explicitly constraining blend weights to reasonable values and having a run-time cost that is nearly independent of the number of example motions. We present experimental results on a test data set of 37,000 frames, or about ten minutes of motion sampled at 60 Hz.

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Index Terms

Automated extraction and parameterization of motions in large data sets
1. Computing methodologies
  1. Computer graphics
    1. Animation

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cover image ACM Conferences

SIGGRAPH '04: ACM SIGGRAPH 2004 Papers

August 2004

684 pages

ISBN:9781450378239

DOI:10.1145/1186562

Editor:
Joe Marks
Mitsubishi Electric Research Laboratories (MERL)

Copyright © 2004 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: 01 August 2004

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SIGGRAPH04: Special Interest Group on Computer Graphics and Interactive Techniques

August 8 - 12, 2004

California, Los Angeles

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SIGGRAPH '04 Paper Acceptance Rate 83 of 478 submissions, 17%;

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

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https://doi.org/10.1038/s41597-023-02076-4
Starke SMason IKomura T(2022)DeepPhaseACM Transactions on Graphics10.1145/3528223.353017841:4(1-13)Online publication date: Jul-2022
https://doi.org/10.1145/3528223.3530178
Valle-Pérez GHenter GBeskow JHolzapfel AOudeyer PAlexanderson S(2021)TransflowerACM Transactions on Graphics10.1145/3478513.348057040:6(1-14)Online publication date: 10-Dec-2021
https://dl.acm.org/doi/10.1145/3478513.3480570
Cho KKim CPark JPark JNoh J(2021)Motion recommendation for online character controlACM Transactions on Graphics10.1145/3478513.348051240:6(1-16)Online publication date: 10-Dec-2021
https://dl.acm.org/doi/10.1145/3478513.3480512
Yoshida NLiu JShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)On-demand Action Detection System using Pose InformationProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3478567(2810-2812)Online publication date: 17-Oct-2021
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Starke SZhao YKomura TZaman K(2020)Local motion phases for learning multi-contact character movementsACM Transactions on Graphics10.1145/3386569.339245039:4Online publication date: 8-Jul-2020
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Luo YSoeseno JChen TChen W(2020)CARLACM Transactions on Graphics10.1145/3386569.339243339:4(38:1-38:10)Online publication date: 12-Aug-2020
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SHIRAFUJI SOGATA THUANG ZMATSUI NUEDA TMAEDA JKITAJIMA YKANAI-PAK MUMEDA YYANAGISAWA HOTA J(2018)Study of design factors for transfer-aid equipment based on caregivers’ feelingsJournal of Advanced Mechanical Design, Systems, and Manufacturing10.1299/jamdsm.2018jamdsm001312:1(JAMDSM0013-JAMDSM0013)Online publication date: 2018
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Xia GSun HChen BLiu QFeng LZhang GHang R(2018)Nonlinear Low-Rank Matrix Completion for Human Motion RecoveryIEEE Transactions on Image Processing10.1109/TIP.2018.281210027:6(3011-3024)Online publication date: Jun-2018
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Villegas RYang JCeylan DLee H(2018)Neural Kinematic Networks for Unsupervised Motion Retargetting2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition10.1109/CVPR.2018.00901(8639-8648)Online publication date: Jun-2018
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