Two-Character Motion Analysis and Synthesis
In this paper, we deal with the problem of synthesizing novel motions of standing-up martial arts such as kickboxing, karate, and taekwondo performed by a pair of humanlike characters while reflecting their interactions. Adopting an example-based paradigm, we address three nontrivial issues embedded...
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| Veröffentlicht in: | IEEE transactions on visualization and computer graphics 2008-05, Vol.14 (3), p.707-720 |
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| creator | Kwon, Taesoo Cho, Young-Sang Park, Sang Il Shin, Sung Yong |
| description | In this paper, we deal with the problem of synthesizing novel motions of standing-up martial arts such as kickboxing, karate, and taekwondo performed by a pair of humanlike characters while reflecting their interactions. Adopting an example-based paradigm, we address three nontrivial issues embedded in this problem: motion modeling, interaction modeling, and motion synthesis. For the first issue, we present a semiautomatic motion-labeling scheme based on force-based motion segmentation and learning-based action classification. We also construct a pair of motion transition graphs, each of which represents an individual motion stream. For the second issue, we propose a scheme for capturing the interactions between two players. A dynamic Bayesian network is adopted to build a motion transition model on top of the coupled motion transition graph that is constructed from an example motion stream. For the last issue, we provide a scheme for synthesizing a novel sequence of coupled motions, guided by the motion transition model. Although the focus of the present work is on martial arts, we believe that the framework of the proposed approach can be conveyed to other two-player motions as well. |
| doi_str_mv | 10.1109/TVCG.2008.22 |
| format | Article |
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Adopting an example-based paradigm, we address three nontrivial issues embedded in this problem: motion modeling, interaction modeling, and motion synthesis. For the first issue, we present a semiautomatic motion-labeling scheme based on force-based motion segmentation and learning-based action classification. We also construct a pair of motion transition graphs, each of which represents an individual motion stream. For the second issue, we propose a scheme for capturing the interactions between two players. A dynamic Bayesian network is adopted to build a motion transition model on top of the coupled motion transition graph that is constructed from an example motion stream. For the last issue, we provide a scheme for synthesizing a novel sequence of coupled motions, guided by the motion transition model. 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Although the focus of the present work is on martial arts, we believe that the framework of the proposed approach can be conveyed to other two-player motions as well.</description><subject>Algorithms</subject><subject>Animation</subject><subject>Arm</subject><subject>Arts</subject><subject>Bayesian methods</subject><subject>Classification</subject><subject>Computer Graphics</subject><subject>Computer Simulation</subject><subject>Computer vision</subject><subject>Construction</subject><subject>Conveying</subject><subject>Graphs</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Layout</subject><subject>Leg</subject><subject>Martial Arts</subject><subject>Models, Biological</subject><subject>Motion analysis</subject><subject>Motion segmentation</subject><subject>Movement - physiology</subject><subject>Network synthesis</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Segmentation</subject><subject>Statistical</subject><subject>Streams</subject><subject>Synthesis</subject><subject>Whole Body Imaging - methods</subject><issn>1077-2626</issn><issn>1941-0506</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqFkc1Lw0AQxRdRbK3evAkSPOhBU2f2M3ssQatQ8WD1GjbJlqakSc0mSP97t7QoeLCn2WF_vHkzj5BzhCEi6PvpRzweUoBoSOkB6aPmGIIAeejfoFRIJZU9cuLcAgA5j_Qx6WHEpKZK9Mnt9KsO47lpTNbaJnip26KuglFlyrUrXGCqPHhbV-3c-u6UHM1M6ezZrg7I--PDNH4KJ6_j53g0CTPOsA1zoS0KZqxQLFKKUi4k8lwLrlQOkKPOtDdmUoNZpiMl0Kaaa7SKyxQwYwNys9VdNfVnZ12bLAuX2bI0la07l2hgkkst9F7Si_tJSkWevP6XVMA5amR7QeZP6LcDD179ARd11_jD-bGSgrfn9x-Quy2UNbVzjZ0lq6ZYmmadICSb-JJNfMkmvoRSj1_uNLt0afNfeJeXBy62QGGt_fnm3rsCxb4B0z-Zmg</recordid><startdate>20080501</startdate><enddate>20080501</enddate><creator>Kwon, Taesoo</creator><creator>Cho, Young-Sang</creator><creator>Park, Sang Il</creator><creator>Shin, Sung Yong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Animation Arm Arts Bayesian methods Classification Computer Graphics Computer Simulation Computer vision Construction Conveying Graphs Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Layout Leg Martial Arts Models, Biological Motion analysis Motion segmentation Movement - physiology Network synthesis Pattern Recognition, Automated - methods Segmentation Statistical Streams Synthesis Whole Body Imaging - methods |
| title | Two-Character Motion Analysis and Synthesis |
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