Abstract
Analysis of human motion is an important research area in computer vision with numerous applications. Recent projects, such as EU i-Treasures and TERPSICHORE projects conduct research in this field to improve the capture, analysis and presentation of Intangible Cultural Heritage (ICH) using ICT-based approaches. The final goal is to document these forms of intangible heritage and to capture the associated knowledge in order to safeguard and transmit this information to the next generations. In addition, these approaches can give rise to new services for research, education and cultural tourism. They can also be used by creative industries (e.g. companies performing film, video, TV or VR applications production), as well as by local communities, creating new local development opportunities by promoting local heritage. This paper first reviews some very recent state of the art approaches based on deep learning which can achieve impressive results in recovering human motion (2D or 3D) and structure (skeleton with joints or realistic 3D model of the human body). Based on such approaches, we then propose a dance analysis approach, currently under development in TERPSICHORE project. Preliminary results are presented and, finally, some conclusions are drawn.
Keywords
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Moeslund TB, Hilton A, Kruger V (2006) A survey of advances in vision-based human motion capture and analysis. Comput Vis Image Und 104:90–126. https://doi.org/10.1016/j.cviu.2006.08.002
Andriluka M, Pishchulin L, Gehler P, Schiele B (2014) 2D human pose estimation: new benchmark and state of the art analysis. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), Columbus, OH, USA, June 23–28, 2014. IEEE, Columbus, OH
Cao Z, Simon T, Wei SE, Sheikh Y (2017) Realtime multi-person 2D pose estimation using part affinity fields. In: The IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, July 21–26, 2017. IEEE, Honolulu, HI
Wei SE, Ramakrishna V, Kanade T, Sheikh Y (2016) Convolutional pose machines. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, June 27–30, 2016. IEEE, Las Vegas, NV, pp 4724–4732
Simon T, Joo H, Matthews I, Sheikh Y (2017) Hand keypoint detection in single images using multiview bootstrapping. In: The IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, July 21–26, 2017, vol 2. IEEE, Honolulu, HI
Zhou X, Huang Q, Sun X, Xue X, Wei Y (2017) Towards 3D human pose estimation in the wild: a weakly-supervised approach. In: 2017 IEEE international conference on computer vision (ICCV), Venice, Italy, October 22–29, 2017. IEEE, Venice
Newell A, Yang K, Deng J (2016) Stacked hourglass networks for human pose estimation. In: Liebe B, Matas J, Sebe N, Welling M (eds) Proceedings of 14th European conference, European conference on computer vision (ECCV) 2016, Amsterdam, The Netherlands, October 11–14, 2016. Springer, Cham, pp 483–499
Mehta D, Sridhar S, Sotnychenko O, Rhodin H, Shafiei M, Seidel HP et al (2017) Real-time 3D human pose estimation with a single RGB camera. ACM Trans Graph 36(4):44. https://doi.org/10.1145/3072959.3073596
Mehta D, Rhodin H, Casas D, Fua P, Sotnychenko O, Xu W, Theobalt C (2017) Monocular 3D human pose estimation in the wild using improved CNN supervision. In: 2017 International conference on 3D vision (3DV), Qingdao, China, October 10–12, 2017. IEEE, Qingdao, pp 506–516
Güler RA, Neverova N, Kokkinos I (2018) DensePose: dense human pose estimation in the wild. In: Proc. CVPR
Güler RA, Trigeorgis G, Antonakos E, Snape P, Zafeiriou S, Kokkinos I (2017) DenseReg: fully convolutional dense shape regression in-the-wild. In: The IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, July 21–26, 2017. IEEE, Honolulu, HI
He K, Gkioxari G, Dollar P, Girshick R, Mask R-CNN (2017) Proceedings of IEEE international conference on computer vision (ICCV), Venice, Italy, October 22–29, 2017. IEEE, Venice
Kanazawa A, Black MJ, Jacobs DW, Malik J (2018) End-to-end recovery of human shape and pose. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 7122–7131
Loper M, Mahmood N, Romero J, Pons-Moll G, Black MJ (2015) SMPL: a skinned multi-person linear model. ACM Trans Graph 34(6):248:1–248:16, https://doi.org/10.1145/2816795.2818013
Gong W, Zhang X, Gonzalez J, Sobral A, Bouwmans T, Tu C, Zahzah E (2016) Human pose estimation from monocular images: a comprehensive survey. Sensors 16(12):1996. https://doi.org/10.3390/s16121966
Ke S, Thuc HLU, Lee YJ, Hwang JN, Yoo JH, Choi KH (2013) A review on video-based human activity recognition. Computers 2(2):88–131. https://doi.org/10.3390/computers2020088
Neverova N (2016) Deep Learning for Human Motion Analysis. PhD Thesis. Universite de Lyon, Lyon. https://doi.org/10.13140/RG.2.1.1255.8961
Chan C, Ginosar S, Zhou T, Efros AA (2018) Everybody dance now. arXiv preprint arXiv:1808.07371
Wang T-C, Liu M-Y, Zhu J-Y, Tao A, Kautz J, Catanzaro B (2017) High-resolution image synthesis and semantic manipulation with conditional GANs. arXiv preprint arXiv:1711.11585
Martinez J, Hossain R, Romero J, Little JJ (2017) A simple yet effective baseline for 3d human pose estimation. In: ICCV
Yasunori K, Ogaki K, Matsui Y, Odagiri Y (2018) Unsupervised adversarial learning of 3D human pose from 2D joint locations. arXiv preprint arXiv:1803.08244
Newell A, Yang K, Deng J (2016) Stacked hourglass networks for human pose estimation. In: European conference on computer vision. Springer, Cham, pp 483–499
Hutchinson-Guest AD (1989) Choreo-graphics: a comparison of dance notation systems from 15th century to the present. In: International conference exploring research. Routledge, New York, p 194
Laban R (1928) Schrifttanz. Universal, Wein
Crnkovic-Friis L (2016) Generative choreography using deep learning. In: 7th International conference on computational creativity, ICCC2016
Dimitropoulos K, Tsalakanidou F, Nikolopoulos S, Kompatsiaris I, Grammalidis N, Manitsaris S, Hadjileontiadis L (2018) A multimodal approach for the safeguarding and transmission of intangible cultural heritage: the case of i-treasures. IEEE Intell Syst 33(6):3–16
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Grammalidis, N., Kico, I., Liarokapis, F. (2020). Analysis of Human Motion Based on AI Technologies: Applications for Safeguarding Folk Dance Performances. In: Kavoura, A., Kefallonitis, E., Theodoridis, P. (eds) Strategic Innovative Marketing and Tourism. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-36126-6_35
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