Audio-driven human body motion analysis and synthesis

F. Ofli, C. Canton-Ferrer, J. Tilmanne, Y. Demir, E. Bozkurt, Y. Yemez, E. Erzin, A. M. Tekalp
<span title="">2008</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="" style="color: black;">Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing</a> </i> &nbsp;
This paper presents a framework for audio-driven human body motion analysis and synthesis. We address the problem in the context of a dance performance, where gestures and movements of the dancer are mainly driven by a musical piece and characterized by the repetition of a set of dance !gures. The system is trained in a supervised manner using the multiview video recordings of the dancer. The human body posture is extracted from multiview video information without any human intervention using a
more &raquo; ... novel marker-based algorithm based on annealing particle !ltering. Audio is analyzed to extract beat and tempo information. The joint analysis of audio and motion features provides a correlation model that is then used to animate a dancing avatar when driven with any musical piece of the same genre. Results are provided showing the effectiveness of the proposed algorithm.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1109/icassp.2008.4518089</a> <a target="_blank" rel="external noopener" href="">dblp:conf/icassp/OfliCTDBYET08</a> <a target="_blank" rel="external noopener" href="">fatcat:73prooupprfn7icvfmifvhfusi</a> </span>
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