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A multi-modal dance corpus for research into interaction between humans in virtual environments

Essid, Slim, Lin, Xinyu, Gowing, Marc, Kordelas, Georgios, Aksay, Anil, Kelly, Philip, Fillon, Thomas, Zhang, Qianni, Dielmann, Alfred, Kitanovski, Vlado, Tournemenne, Robin, Masurelle, Aymeric, Izquierdo, Ebroul orcid logoORCID: 0000-0002-7142-3970, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135, Daras, Petros orcid logoORCID: 0000-0003-3814-6710 and Richard, Gaël (2012) A multi-modal dance corpus for research into interaction between humans in virtual environments. Journal on Multimodal User Interfaces, 7 (1-2). pp. 157-170. ISSN 1783-7677

We present a new, freely available, multimodal corpus for research into, amongst other areas, real-time realistic interaction between humans in online virtual environments. The specific corpus scenario focuses on an online dance class application scenario where students, with avatars driven by whatever 3D capture technology is locally available to them, can learn choreographies with teacher guidance in an online virtual dance studio. As the dance corpus is focused on this scenario, it consists of student/teacher dance choreographies concurrently captured at two different sites using a variety of media modalities, including synchronised audio rigs, multiple cameras, wearable inertial measurement devices and depth sensors. In the corpus, each of the several dancers performs a number of fixed choreographies, which are graded according to a number of specific evaluation criteria. In addition, ground-truth dance choreography annotations are provided. Furthermore, for unsynchronised sensor modalities, the corpus also includes distinctive events for data stream synchronisation. The total duration of the recorded content is 1 h and 40 min for each single sensor, amounting to 55 h of recordings across all sensors. Although the dance corpus is tailored specifically for an online dance class application scenario, the data is free to download and use for any research and development purposes.
Item Type:Article (Published)
Uncontrolled Keywords:Multimodal data; Computer vision
Subjects:Computer Science > Machine learning
Computer Science > Visualization
Computer Science > Algorithms
Computer Science > Image processing
DCU Faculties and Centres:Research Institutes and Centres > CLARITY: The Centre for Sensor Web Technologies
Official URL:http://link.springer.com/article/10.1007/s12193-01...
Copyright Information:© 2012 Springer-Verlag The original publication is available at www.springerlink.com
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:17916
Deposited On:12 Apr 2013 10:34 by David Monaghan . Last Modified 15 Nov 2018 11:39

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