@article{ ISI:000208536800003,
Author = {Ofli, Ferda and Demir, Yasemin and Yemez, Yucel and Erzin, Engin and
   Tekalp, A. Murat and Balci, Koray and Kizoglu, Idil and Akarun, Lale and
   Canton-Ferrer, Cristian and Tilmanne, Joelle and Bozkurt, Elif and
   Erdem, A. Tanju},
Title = {{An audio-driven dancing avatar}},
Journal = {{JOURNAL ON MULTIMODAL USER INTERFACES}},
Year = {{2008}},
Volume = {{2}},
Number = {{2, SI}},
Pages = {{93-103}},
Month = {{SEP}},
Abstract = {{We present a framework for training and synthesis of an audio-driven
   dancing avatar. The avatar is trained for a given musical genre using
   the multicamera video recordings of a dance performance. The video is
   analyzed to capture the time-varying posture of the dancer's body
   whereas the musical audio signal is processed to extract the beat
   information. We consider two different marker-based schemes for the
   motion capture problem. The first scheme uses 3D joint positions to
   represent the body motion whereas the second uses joint angles. Body
   movements of the dancer are characterized by a set of recurring semantic
   motion patterns, i.e., dance figures. Each dance figure is modeled in a
   supervised manner with a set of HMM (Hidden Markov Model) structures and
   the associated beat frequency. In the synthesis phase, an audio signal
   of unknown musical type is first classified, within a time interval,
   into one of the genres that have been learnt in the analysis phase,
   based on mel frequency cepstral coefficients (MFCC). The motion
   parameters of the corresponding dance figures are then synthesized via
   the trained HMM structures in synchrony with the audio signal based on
   the estimated tempo information. Finally, the generated motion
   parameters, either the joint angles or the 3D joint positions of the
   body, are animated along with the musical audio using two different
   animation tools that we have developed. Experimental results demonstrate
   the effectiveness of the proposed framework.}},
DOI = {{10.1007/s12193-008-0009-x}},
ISSN = {{1783-7677}},
ResearcherID-Numbers = {{Erzin, Engin/H-1716-2011}},
ORCID-Numbers = {{Erzin, Engin/0000-0002-2715-2368}},
Unique-ID = {{ISI:000208536800003}},
}

{"_id":"e3Kvmby2jPzf99Fw7","bibbaseid":"ofli-demir-yemez-erzin-tekalp-balci-kizoglu-akarun-etal-anaudiodrivendancingavatar-2008","downloads":0,"creationDate":"2016-12-09T13:41:59.858Z","title":"An audio-driven dancing avatar","author_short":["Ofli, F.","Demir, Y.","Yemez, Y.","Erzin, E.","Tekalp, A. M.","Balci, K.","Kizoglu, I.","Akarun, L.","Canton-Ferrer, C.","Tilmanne, J.","Bozkurt, E.","Erdem, A. T."],"year":2008,"bibtype":"article","biburl":"https://drive.google.com/uc?export=download&id=1d5Wvl98W_buJq6prXBz16a_6uF9Cdt-7","bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Ofli"],"firstnames":["Ferda"],"suffixes":[]},{"propositions":[],"lastnames":["Demir"],"firstnames":["Yasemin"],"suffixes":[]},{"propositions":[],"lastnames":["Yemez"],"firstnames":["Yucel"],"suffixes":[]},{"propositions":[],"lastnames":["Erzin"],"firstnames":["Engin"],"suffixes":[]},{"propositions":[],"lastnames":["Tekalp"],"firstnames":["A.","Murat"],"suffixes":[]},{"propositions":[],"lastnames":["Balci"],"firstnames":["Koray"],"suffixes":[]},{"propositions":[],"lastnames":["Kizoglu"],"firstnames":["Idil"],"suffixes":[]},{"propositions":[],"lastnames":["Akarun"],"firstnames":["Lale"],"suffixes":[]},{"propositions":[],"lastnames":["Canton-Ferrer"],"firstnames":["Cristian"],"suffixes":[]},{"propositions":[],"lastnames":["Tilmanne"],"firstnames":["Joelle"],"suffixes":[]},{"propositions":[],"lastnames":["Bozkurt"],"firstnames":["Elif"],"suffixes":[]},{"propositions":[],"lastnames":["Erdem"],"firstnames":["A.","Tanju"],"suffixes":[]}],"title":"An audio-driven dancing avatar","journal":"JOURNAL ON MULTIMODAL USER INTERFACES","year":"2008","volume":"2","number":"2, SI","pages":"93-103","month":"SEP","abstract":"We present a framework for training and synthesis of an audio-driven dancing avatar. The avatar is trained for a given musical genre using the multicamera video recordings of a dance performance. The video is analyzed to capture the time-varying posture of the dancer's body whereas the musical audio signal is processed to extract the beat information. We consider two different marker-based schemes for the motion capture problem. The first scheme uses 3D joint positions to represent the body motion whereas the second uses joint angles. Body movements of the dancer are characterized by a set of recurring semantic motion patterns, i.e., dance figures. Each dance figure is modeled in a supervised manner with a set of HMM (Hidden Markov Model) structures and the associated beat frequency. In the synthesis phase, an audio signal of unknown musical type is first classified, within a time interval, into one of the genres that have been learnt in the analysis phase, based on mel frequency cepstral coefficients (MFCC). The motion parameters of the corresponding dance figures are then synthesized via the trained HMM structures in synchrony with the audio signal based on the estimated tempo information. Finally, the generated motion parameters, either the joint angles or the 3D joint positions of the body, are animated along with the musical audio using two different animation tools that we have developed. Experimental results demonstrate the effectiveness of the proposed framework.","doi":"10.1007/s12193-008-0009-x","issn":"1783-7677","researcherid-numbers":"Erzin, Engin/H-1716-2011","orcid-numbers":"Erzin, Engin/0000-0002-2715-2368","unique-id":"ISI:000208536800003","bibtex":"@article{ ISI:000208536800003,\nAuthor = {Ofli, Ferda and Demir, Yasemin and Yemez, Yucel and Erzin, Engin and\n Tekalp, A. Murat and Balci, Koray and Kizoglu, Idil and Akarun, Lale and\n Canton-Ferrer, Cristian and Tilmanne, Joelle and Bozkurt, Elif and\n Erdem, A. Tanju},\nTitle = {{An audio-driven dancing avatar}},\nJournal = {{JOURNAL ON MULTIMODAL USER INTERFACES}},\nYear = {{2008}},\nVolume = {{2}},\nNumber = {{2, SI}},\nPages = {{93-103}},\nMonth = {{SEP}},\nAbstract = {{We present a framework for training and synthesis of an audio-driven\n dancing avatar. The avatar is trained for a given musical genre using\n the multicamera video recordings of a dance performance. The video is\n analyzed to capture the time-varying posture of the dancer's body\n whereas the musical audio signal is processed to extract the beat\n information. We consider two different marker-based schemes for the\n motion capture problem. The first scheme uses 3D joint positions to\n represent the body motion whereas the second uses joint angles. Body\n movements of the dancer are characterized by a set of recurring semantic\n motion patterns, i.e., dance figures. Each dance figure is modeled in a\n supervised manner with a set of HMM (Hidden Markov Model) structures and\n the associated beat frequency. In the synthesis phase, an audio signal\n of unknown musical type is first classified, within a time interval,\n into one of the genres that have been learnt in the analysis phase,\n based on mel frequency cepstral coefficients (MFCC). The motion\n parameters of the corresponding dance figures are then synthesized via\n the trained HMM structures in synchrony with the audio signal based on\n the estimated tempo information. Finally, the generated motion\n parameters, either the joint angles or the 3D joint positions of the\n body, are animated along with the musical audio using two different\n animation tools that we have developed. Experimental results demonstrate\n the effectiveness of the proposed framework.}},\nDOI = {{10.1007/s12193-008-0009-x}},\nISSN = {{1783-7677}},\nResearcherID-Numbers = {{Erzin, Engin/H-1716-2011}},\nORCID-Numbers = {{Erzin, Engin/0000-0002-2715-2368}},\nUnique-ID = {{ISI:000208536800003}},\n}\n\n","author_short":["Ofli, F.","Demir, Y.","Yemez, Y.","Erzin, E.","Tekalp, A. M.","Balci, K.","Kizoglu, I.","Akarun, L.","Canton-Ferrer, C.","Tilmanne, J.","Bozkurt, E.","Erdem, A. 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