Andreas Aristidou
Efstathios Stavrakis
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Abstract
Motion capture (mocap) technology is an efficient method for digitizing art performances, and is becoming increasingly popular in the preservation and dissemination of dance performances. Although technically the captured data can be of very high quality, dancing allows stylistic variations and improvisations that cannot be easily identified. The majority of motion analysis algorithms are based on ad-hoc quantitative metrics, thus do not usually provide insights on style qualities of a performance. In this work, we present a framework based on the principles of Laban Movement Analysis (LMA) that aims to identify style qualities in dance motions. The proposed algorithm uses a feature space that aims to capture the four LMA components (Body, Effort, Shape, Space), and can be subsequently used for motion comparison and evaluation. We have designed and implemented a prototype virtual reality simulator for teaching folk dances in which users can preview dance segments performed by a 3D avatar and repeat them. The user’s movements are captured and compared to the folk dance template motions; then, intuitive feedback is provided to the user based on the LMA components. The results demonstrate the effectiveness of our system, opening new horizons for automatic motion and dance evaluation processes.
- Dimitrios S. Alexiadis and Petros Daras. 2014. Quaternionic signal processing techniques for automatic evaluation of dance performances from MoCap data. IEEE Transactions on Multimedia 99, 1--16.Google Scholar
- Andreas Aristidou and Yiorgos Chrysanthou. 2013. Motion indexing of different emotional states using LMA components. In SIGGRAPH Asia 2013 Technical Briefs (SA’13). ACM, New York, NY, 21:1--21:4. Google ScholarDigital Library
- Andreas Aristidou and Yiorgos Chrysanthou. 2014. Feature extraction for human motion indexing of acted dance performances. In Proceedings of the 9th International Conference on Computer Graphics Theory and Applications (GRAPP’14). 277--287.Google Scholar
- Andreas Aristidou, Efstathios Stavrakis, and Yiorgos Chrysanthou. 2014. LMA-based motion retrieval for folk dance cultural heritage. In Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection. LNCS, Vol. 8740. Springer International Publishing, Cham, Switzerland, 207--216.Google Scholar
- J. C. P. Chan, H. Leung, J. K. T. Tang, and T. Komura. 2011. A virtual reality dance training system using motion capture technology. IEEE Transactions on Learning Technologies 4, 2, 187--195. Google ScholarDigital Library
- Min-Wen Chao, Chao-Hung Lin, J. Assa, and Tong-Yee Lee. 2012. Human motion retrieval from hand-drawn sketch. IEEE Transactions on Visualization and Computer Graphics 18, 5, 729--740. Google ScholarDigital Library
- J. Chen, W. Lin, K. Tsai, and S. Dai. 2011. Analysis and evaluation of human movement based on Laban movement analysis. Tamkang Journal of Science and Engineering 14, 3, 255--264.Google Scholar
- Diane Chi, Monica Costa, Liwei Zhao, and Norman Badler. 2000. The EMOTE model for effort and shape. In Proceedings of SIGGRAPH’00. ACM, New York, NY, 173--182. Google ScholarDigital Library
- Emily S. Cross, David J. M. Kraemer, Antonia F. de C. Hamilton, William M. Kelley, and Scott T. Grafton. 2009. Sensitivity of the action observation network to physical and observational learning. Cerebral Cortex (New York, NY) 19, 2, 315--326.Google Scholar
- Liqun Deng, Howard Leung, Naijie Gu, and Yang Yang. 2011. Real-time mocap dance recognition for an interactive dancing game. Computer Animation and Virtual Worlds 22, 2--3, 229--237. Google ScholarDigital Library
- Zhigang Deng, Qin Gu, and Qing Li. 2009. Perceptually consistent example-based human motion retrieval. In Proceedings of I3D’09. ACM, New York, NY, 191--198. Google ScholarDigital Library
- Björn Hartmann, Maurizio Mancini, and Catherine Pelachaud. 2006. Implementing expressive gesture synthesis for embodied conversational agents. In Proceedings of GW’05. Springer, Berlin, 188--199. Google ScholarDigital Library
- Mubbasir Kapadia, I-kao Chiang, Tiju Thomas, Norman I. Badler, and Joseph T. Kider, Jr. 2013. Efficient motion retrieval in large motion databases. In Proceedings of I3D’13. ACM, New York, NY, 19--28. Google ScholarDigital Library
- Gayle Kassing and Danielle Mary Jay. 2003. Dance Teaching Methods and Curriculum Design. Human Kinetics Publishers, Champaign, IL.Google Scholar
- Eamonn Keogh, Themistoklis Palpanas, Victor B. Zordan, Dimitrios Gunopulos, and Marc Cardle. 2004. Indexing large human-motion databases. In Proceedings of VLDB. 780--791. Google ScholarDigital Library
- Alexandros Kitsikidis, Kosmas Dimitropoulos, Erdal Yilmaz, Stella Douka, and Nikos Grammalidis. 2014. Multi-sensor technology and fuzzy logic for dancer motion analysis and performance evaluation within a 3D virtual environment. In Design and Development Methods for Universal Access in Human--Computer Interaction. LNCS, Vol. 8513. Springer International Publishing, Cham, Switzerland, 379--390.Google Scholar
- Lucas Kovar and Michael Gleicher. 2004. Automated extraction and parameterization of motions in large data sets. Transactions on Graphics 23, 3, 559--568. Google ScholarDigital Library
- Björn Krüger, Jochen Tautges, Andreas Weber, and Arno Zinke. 2010. Fast local and global similarity searches in large motion capture databases. In Proceedings of SCA’10. 1--10. Google ScholarDigital Library
- Rudolf Laban. 2011. The Mastery of Movement (4 ed.). Dance Books Ltd., Binsted, Hampshire, UK.Google Scholar
- Nadia Magnenat-Thalmann, Dimitrios Protopsaltou, and Evangelia Kavakli. 2008. Learning how to dance using a web 3D platform. In Advances in Web Based Learning—ICWL 2007. LNCS, Vol. 4823. Springer, Berlin, 1--12. Google ScholarDigital Library
- Valeri Maletić. 1987. Body, Space, Expression: The Development of Rudolf Laban’s Movement and Dance Concepts. De Gruyter Mouton, Berlin.Google Scholar
- Megumi Masuda, Shohei Kato, and Hidenori Itoh. 2009. Emotion detection from body motion of human form robot based on Laban movement analysis. In Principles of Practice in Multi-Agent Systems. LNCS, Vol. 5925. Springer, Berlin, 322--334. Google ScholarDigital Library
- Megumi Masuda, Shohei Kato, and Hidenori Itoh. 2010. Laban-based motion rendering for emotional expression of human form robots. In Knowledge Management and Acquisition for Smart Systems and Services. LNCS, Vol. 6232. Springer, Berlin, 49--60. Google ScholarDigital Library
- Meinard Müller, Tido Röder, and Michael Clausen. 2005. Efficient content-based retrieval of motion capture data. Transactions on Graphics 24, 3, 677--685. Google ScholarDigital Library
- Seiji Okajima, Yuki Wakayama, and Yoshihiro Okada. 2012. Human motion retrieval system based on LMA features using IEC method. In Innovations in Intelligent Machines. Studies in Computational Intelligence. Vol. 376, Springer, Berlin, 117--130.Google Scholar
- Karl Pearson. 1920. Notes on the history of correlation. Biometrika 13, 1, 25--45.Google ScholarCross Ref
- PhaseSpace 2014. PhaseSpace Inc: Optical Motion Capture Systems. Retrieved June 11, 2015 from http://www.phasespace.com.Google Scholar
- Luis Santos and Jorge Dias. 2010. Laban movement analysis towards behavior patterns. In Emerging Trends in Technological Innovation. Advances in Information and Communication Technology, Vol. 314. Springer, Berlin, 187--194.Google Scholar
- Takaaki Shiratori, Atsushi Nakazawa, and Katsushi Ikeuchi. 2006. Dancing-to-music character animation. Computer Graphics Forum 25, 3, 449--458.Google ScholarCross Ref
- Efstathios Stavrakis, Andreas Aristidou, Maria Savva, Stephania Loizidou Himona, and Yiorgos Chrysanthou. 2012. Digitization of Cypriot folk dances. In Proceedings of the 4th International Conference on Progress in Cultural Heritage Preservation (EuroMed’12) (October 29--November 3), Vol. 7616. LNCS, Limassol, Cyprus, 404--413. Google ScholarDigital Library
- Jeff K. T. Tang, Jacky C. P. Chan, and Howard Leung. 2011. Interactive dancing game with real-time recognition of continuous dance moves from 3D human motion capture. In Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication (ICUIMC’11). ACM, New York, NY, 50:1--50:9. Google ScholarDigital Library
- Joshua B. Tenenbaum, Vin De Silva, and John C. Langford. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290, 5500, 2319--2323.Google Scholar
- Lorenzo Torresani, Peggy Hackney, and Christoph Bregler. 2006. Learning motion style synthesis from perceptual observations. In Proceedings of NIPS’06. 1393--1400.Google Scholar
- Arthur Truong, Hugo Boujut, and Titus Zaharia. 2015. Laban descriptors for gesture recognition and emotional analysis. The Visual Computer, 1--16.Google Scholar
- University of Cyprus. Dance Motion Capture Database: Retrieved June 11, 2015 from http://dancedb.cs.ucy.ac.cy/.Google Scholar
- Yuki Wakayama, Seiji Okajima, Shigeru Takano, and Yoshihiro Okada. 2010. IEC-based motion retrieval system using Laban movement analysis. In Proceedings of KES’10. 251--260. Google ScholarDigital Library
- Shuangyuan Wu, Zhaoqi Wang, and Shihong Xia. 2009. Indexing and retrieval of human motion data by a hierarchical tree. In Proceedings of VRST. ACM, New York, NY, 207--214. Google ScholarDigital Library
- Yang Yang, Howard Leung, Lihua Yue, and Liqun Deng. 2013. Generating a two-phase lesson for guiding beginners to learn basic dance movements. Computers & Education 61, 1--20. Google ScholarDigital Library
- Haris Zacharatos, Christos Gatzoulis, Yiorgos Chrysanthou, and Andreas Aristidou. 2013. Emotion recognition for exergames using Laban movement analysis. In Proceedings of Motion in Games (MIG’13). ACM, New York, NY, 39--44. Google ScholarDigital Library
- Liwei Zhao and Norman I. Badler. 2005. Acquiring and validating motion qualities from live limb gestures. Graphical Models 67, 1, 1--16. Google ScholarDigital Library
Index Terms
- Folk Dance Evaluation Using Laban Movement Analysis
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