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    A Practical Approach to Using Motion Capture in Performance Dance

    Electronic Visualisation and the Arts (EVA 2016)

    London, UK, 12 - 14 July 2016


    Paul Golz, Chris P. Bowers & Marc Price



    Motion capture is a key element of many aspects of digital performance including virtual and augmented realities (Golz & Shaw 2014; Chan, Leung, Tang & Komura 2011), digital art and visualisation (Gibson 2011). Extensive research and praxis exists on markered systems within live performance however the use of markers necessarily changes the aesthetic of the performance work (Hutchison & Vincs 2013). Whilst markerless motion capture systems are available, traditionally there has been significant overhead in terms of hardware, set-up, processing power and cost. However, with the introduction of low-cost consumer depth sensors such as Microsoft’s Kinect in 2010 and Kinect2 in 2013, artists are now exploring the possibility of real-time motion capture within live performance applications (Golz & Smith-Nunes 2015).

    A key element of motion capture within live performance is the ability to carry out real-time processing of the captured data. Indeed, a perceived advantage of the Kinect hardware is the availability of low-bandwidth, pre-processed skeletal model data. Here we will highlight the capabilities and limitations of various data streams produced by the Kinect2 within live dance performance. Whilst this work is applicable to all performance arts, dance is traditionally difficult to accurately motion capture due to the innate characteristics of the form. These include the dynamic and spatial range, multiple performers, contact and occlusion between performers, use of inversion and floor work and the presence of props.


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