Article

Motion modeling for on-line locomotion synthesis

Authors:

Sung Yong ShinAuthors Info & Claims

SCA '05: Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation

Pages 29 - 38

https://doi.org/10.1145/1073368.1073373

Published: 29 July 2005 Publication History

Abstract

In this paper, we propose an example-based approach to on-line locomotion synthesis. Our approach consists of two parts: motion analysis and motion synthesis. In the motion analysis part, an unlabeled motion sequence is first decomposed into motion segments, exploiting the behavior of the COM (center of mass) trajectory of the performer. Those motion segments are subsequently classified into groups of motion segments such that the same group of motion segments share an identical footstep pattern. Finally, we construct a hierarchical motion transition graph by representing these groups and their connectivity to other groups as nodes and edges, respectively. The coarse level of this graph models locomotive motions and their transitions, and the fine level mainly captures the cyclic nature of locomotive motions. In the motion synthesis part, given a stream of motion specifications in an on-line manner, the motion transition graph is traversed while blending the motion segments to synthesize a motion at a node, one by one, guided by the motion specifications. Our main contributions are the motion labeling scheme and a new motion model, embodied by the hierarchical motion transition graph, which together enable not only artifact-free motion blending but also seamless motion transition.

References

[1]

{ACP02} Allen B., Curless B., Popovic Z.: Articulated body deformation from range scan data. ACM Transactions on Graphics 21, 3 (July 2002), 612--619.]]

Digital Library

[2]

{AF02} Arikan O., Forsyth D. A.: Interactive Motion Generation from Examples. ACM Transactions on Graphics (Proc. SIGGRAPH 2002) 21, 3 (July 2002), 483--490.]]

Digital Library

[3]

{AFO03} Arikan O., Forsyth D. A., O'Brien J. F.: Motion synthesis from annotations. ACM Transactions on Graphics (Proc. SIGGRAPH 2003) 22, 3 (July 2003), 402--408.]]

Digital Library

[4]

{AMN*94} Arya S., Mount D. M., Netanyahu N. S., Silverman R., Wu A.: An optimal algorithm for approximate nearest neighbor searching. In Proceedings of 5th ACM-SIAM Sympos. Discrete Algorithms (1994), pp. 573--582.]]

Digital Library

[5]

{BB98} Bindiganavale R., Badler N. I.: Motion abstraction and mapping with spatial constraints. In Proceedings of international Workshop CAPTECH'98 (1998), pp. 70--82.]]

Digital Library

[6]

{BSP*04} Barbic J., Safonova A., Pan J., Faloutsos C., Hodgins J., Pollard N.: Segmenting motion capture data into distinct behaviors. In In the Proc. of Graphics Interface (2004), pp. 185--194.]]

Digital Library

[7]

{FMJ02} Fod A., Mataric M. J., Jenkins O.: Automated derivation of primitives for movement classification. Autonomous Robots 12, 1 (2002), 39--54.]]

Digital Library

[8]

{GJH00} Galata A., Johnson N., Hogg D.: Learning variable-length markov modles of behavior. Computer Vision and Image Understanding 81 (2000), 398--413.]]

Digital Library

[9]

{GR96} Guo S., Robergé J.: A High-Level Control Mechanism for Human Locomotion Based on Parametric Frame Space Interpolation. In Proceedings of Eurographics Workshop on Computer Animation and Simulation 96 (Aug. 1996), pp. 95--107.]]

Digital Library

[10]

{GSKJ03} Gleicher M., Shin H. J., Kovar L., Jepsen A.: Snap-together motion: Assembling run-time animation. ACM Transactions on Graphics 22, 3 (July 2003), 702--702.]]

Digital Library

[11]

{JM03} Jenkins O., Mataric M.: Automated derivation of behavior vocaburaries for autonomous humanoid motion. In In Proc. of AAMAS'03 (2003), pp. 225--232.]]

Digital Library

[12]

{KG03} Kovar L., Gleicher M.: Flexible automatic motion blending with registration curves. In Eurographics/SIGGRAPH Symposium on Computer Animation (2003), Breen D., Lin M., (Eds.), Eurographics Association, pp. 214--224.]]

Digital Library

[13]

{KG04} Kovar L., Gleicher M.: Automated extraction and parameterization motions in large data sets. vol. 23, pp. 559--568.]]

[14]

{KGP02} Kovar L., Gleicher M., Pighin F.: Motion Graphs. ACM Transactions on Graphics (Proc. SIGGRAPH 2002) 21, 3 (July 2002), 473--482.]]

Digital Library

[15]

{KGS02} Kovar L., Gleicher M., Schreiner J.: Footskate cleanup for motion capture editing. In Proceedings of ACM SIGGRAPH Symposium on Computer Animation (July 2002).]]

Digital Library

[16]

{KPS03} Kim T. H., Park S. I., Shin S. Y.: Rhythmic-motion synthesis based on motion-beat analysis. ACM Transactions on Graphics (Proc. SIGGRAPH 2003) 22, 3 (July 2003), 392--401.]]

Digital Library

[17]

{LCR*02} Lee J., Chai J., Reitsma P. S. A., Hodgins J. K., Pollard N. S.: Interactive Control of Avatars Animated with Human Motion Data. ACM Transactions on Graphics (Proc. SIGGRAPH 2002) 21, 3 (July 2002), 491--500.]]

Digital Library

[18]

{LP02} Liu C. K., Popovic Z.: Synthesis of complex dynamic character motion from simple animations. In SIGGRAPH 2002 Conference Proceedings (2002), Hughes J., (Ed.), Annual Conference Series. ACM Press/ACM SIGGRAPH, pp. 408--416.]]

Digital Library

[19]

{LWS02} Li Y., Wang T., Shum H.: Motion Texture: A Two-Level Statistical Model for Character Motion Synthesis. ACM Transactions on Graphics (Proc. SIGGRAPH 2002) 21, 3 (July 2002), 465--472.]]

Digital Library

[20]

{PB02} Pullen K., Bregler C.: Motion Capture Assisted Animation: Texturing and Synthesis. ACM Transactions on Graphics (Proc. SIGGRAPH 2002) 21, 3 (July 2002), 501--508.]]

Digital Library

[21]

{Per92} Perry J.: Gait analysis: Normal and Pathological Function. Delmar Learning, 1992.]]

[22]

{PSS02} Park S. I., Shin H. J., Shin S. Y.: On-line locomotion generation based on motion blending. In Proceedings of ACM SIGGRAPH Symposium on Computer Animation (July 2002), pp. 105--111.]]

Digital Library

[23]

{PSS04} Park S. I., Shin H. J., Shin S. Y.: On-line motion blending for real-time locomotion generation. Computer Animation and Virtual Worlds 15 (Sept. 2004), 125--138.]]

Digital Library

[24]

{RCB98} Rose C., Cohen M. F., Bodenheimer B.: Verbs and adverbs: Mulidimensional motion interpolation. IEEE Computer Graphics and Applications 18, 5 (Sept. 1998), 32--40.]]

Digital Library

[25]

{SLSG01} Shin H. J., Lee J., Shin S. Y., Gleicher M.: Computer puppetry: An importance-based approach. ACM Trans. Graph. 20, 2 (2001), 67--94.]]

Digital Library

[26]

{SP05} Sulejmanpašić A., Popović J.: Adaptation of performed ballistic motion. ACM Trans. Graph. 24, 1 (2005), 165--179.]]

Digital Library

[27]

{SRC01} Sloan P., Rose C. F., Cohen M. F.: Shape by example. In Proceedings of 2001 ACM Symposium on Interactive 3D Graphics (2001), pp. 135--144.]]

Digital Library

[28]

{TH00} Tanco L. M., Hilton A.: Realistic synthesis of novel human movements from a database of motion capture examples. In Proceedings of the IEEE Workshop on Human Motion (2000), pp. 137--142.]]

Digital Library

[29]

{WH97} Wiley D. J., Hahn J. K.: Interpolation synthesis for articulated fiture motion. IEEE Computer Graphis and Applications 17, 6 (1997), 39--45.]]

Digital Library

[30]

{Win90} Winter D. A.: Biomechanics and Motor Control of Human Movement. John Wiley and Sons Inc, 1990.]]

Cited By

Wang Y(2023)3D Dynamic Image Modeling Based on Machine Learning in Film and Television AnimationJournal of Multimedia Information System10.33851/JMIS.2023.10.1.6910:1(69-78)Online publication date: 30-Mar-2023
https://doi.org/10.33851/JMIS.2023.10.1.69
Zhang R(2023)Using artificial intelligence assistant technology to develop animation games on IoTComputer Science and Information Systems10.2298/CSIS220719021Z20:2(765-792)Online publication date: 2023
https://doi.org/10.2298/CSIS220719021Z
Xia GMa FLiu QZhang D(2023)Pose-Driven Realistic 2-D Motion SynthesisIEEE Transactions on Cybernetics10.1109/TCYB.2021.312001053:4(2412-2425)Online publication date: Apr-2023
https://doi.org/10.1109/TCYB.2021.3120010
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Index Terms

Motion modeling for on-line locomotion synthesis
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        3D imaging
  2. Computer graphics
    1. Animation

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Published In

cover image ACM Conferences

SCA '05: Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation

July 2005

366 pages

ISBN:1595931988

DOI:10.1145/1073368

Conference Chairs:
Demetri Terzopoulos
New York University, USA
,
Victor Brian Zordan
University of California at Riverside, USA
,
Program Chairs:
Ken Anjyo
OLM Digital, Inc., Japan
,
Petros Faloutsos
University of California at Los Angeles, USA

Copyright © 2005 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
EUROGRAPHICS: The European Association for Computer Graphics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 July 2005

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SCA05

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SIGGRAPH
EUROGRAPHICS

SCA05: Symposium on Computer Animation

July 29 - 31, 2005

California, Los Angeles

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Overall Acceptance Rate 183 of 487 submissions, 38%

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Cited By

Wang Y(2023)3D Dynamic Image Modeling Based on Machine Learning in Film and Television AnimationJournal of Multimedia Information System10.33851/JMIS.2023.10.1.6910:1(69-78)Online publication date: 30-Mar-2023
https://doi.org/10.33851/JMIS.2023.10.1.69
Zhang R(2023)Using artificial intelligence assistant technology to develop animation games on IoTComputer Science and Information Systems10.2298/CSIS220719021Z20:2(765-792)Online publication date: 2023
https://doi.org/10.2298/CSIS220719021Z
Xia GMa FLiu QZhang D(2023)Pose-Driven Realistic 2-D Motion SynthesisIEEE Transactions on Cybernetics10.1109/TCYB.2021.312001053:4(2412-2425)Online publication date: Apr-2023
https://doi.org/10.1109/TCYB.2021.3120010
Zhou DFeng XYi PYang XZhang QWei XYang D(2019)3D Human Motion Synthesis Based on Convolutional Neural NetworkIEEE Access10.1109/ACCESS.2019.29176097(66325-66335)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2917609
Hwang JKim JSuh IKwon T(2018)Real‐time Locomotion Controller using an Inverted‐Pendulum‐based Abstract ModelComputer Graphics Forum10.1111/cgf.1336137:2(287-296)Online publication date: 22-May-2018
https://doi.org/10.1111/cgf.13361
Xia GSun HFeng LZhang GLiu Y(2018)Human Motion Segmentation via Robust Kernel Sparse Subspace ClusteringIEEE Transactions on Image Processing10.1109/TIP.2017.273856227:1(135-150)Online publication date: Jan-2018
https://doi.org/10.1109/TIP.2017.2738562
Zhao MYuan YZhang XShi ZWang Y(2018)A novel key frames matching approach for human locomotion interpolationMultimedia Tools and Applications10.1007/s11042-017-4677-y77:6(7779-7794)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s11042-017-4677-y
Lee TKang DKwon T(2018)Motion normalization method based on an inverted pendulum model for clusteringThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-016-1308-y34:1(29-40)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s00371-016-1308-y
Komura THabibie ISchwarz JHolden D(2018)Data-Driven Character Animation SynthesisHandbook of Human Motion10.1007/978-3-319-14418-4_10(2003-2031)Online publication date: 5-Apr-2018
https://doi.org/10.1007/978-3-319-14418-4_10
Asfour TBorràs JMandery CKaiser PAksoy EGrotz M(2017)On the Dualities Between Grasping and Whole-Body Loco-Manipulation TasksRobotics Research10.1007/978-3-319-60916-4_18(305-322)Online publication date: 25-Jul-2017
https://doi.org/10.1007/978-3-319-60916-4_18
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