AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Computational Visual Media Article
PDF (7.4 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Let’s all dance: Enhancing amateur dance motions

School of Computer Science & Technology, Shandong University, Qingdao 266000, China
School of Software, Shandong University, Jinan 250101, China
Department of Computer Science, University of Cyprus, Nicosia 1678, Cyprus; CYENS Centre of Excellence, Nicosia 1016, Cyprus
Qingdao Institute of Humanities and Social Sciences, Shandong University, Qingdao 266000, China

* Qiu Zhou and Manyi Li contributed equally to this work.

Show Author Information

Graphical Abstract

Abstract

Professional dance is characterized by high impulsiveness, elegance, and aesthetic beauty. In order to reach the desired professionalism, it requires years of long and exhausting practice, good physicalcondition, musicality, but also, a good understanding of choreography. Capturing dance motions and transferring them to digital avatars is commonly used in the film and entertainment industries. However, so far, access to high-quality dance data is very limited, mainly due to the many practical difficulties in capturing the movements of dancers, making it prohibitive for large-scale data acquisition. In this paper, we present a model that enhances the professionalism of amateur dance movements, allowing movement quality to be improved in both spatial and temporal domains. Our model consists of a dance-to-music alignment stage responsible for learning the optimal temporal alignment path between dance and music, and a dance-enhancement stage that injects features of professionalism in both spatial and temporal domains. To learn a homogeneous distribution and credible mapping between the heterogeneous professional and amateur datasets, we generate amateur data from professional dances taken from the AIST++dataset. We demonstrate the effectiveness of our method by comparing it with two baseline motion transfer methods via thorough qualitative visual controls, quantitative metrics, and a perceptual study. We also provide temporal and spatial module analysis to examine the mechanisms and necessity of key components of our framework.

Keywords

animationmusic-to-motion alignmentdance motion enhancementdance motion analysis

Electronic Supplementary Material

Video
41095_0292_ESM2.mp4
Download File(s)
41095_0292_ESM1.pdf (2.2 MB)

References

[1]
Hanna, J. L. The Performer-Audience Connection: Emotion to Metaphor in Dance and Society. University of Texas Press, 1983.
[2]
Aristidou, A.; Shamir, A.; Chrysanthou, Y. Digital dance ethnography. Journal on Computing and Cultural Heritage Vol. 12, No. 4, Article No. 29, 2020.
Crossref Google Scholar
[3]
Li, R. L.; Yang, S.; Ross, D. A.; Kanazawa, A. AI choreographer: Music conditioned 3D dance generation with AIST. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 13381–13392, 2021.
Crossref
[4]
Chen, K.; Tan, Z.; Lei, J.; Zhang, S. H.; Guo, Y. C.; Zhang, W.; Hu, S. M. ChoreoMaster: Choreography-oriented music-driven dance synthesis. ACM Transactions on Graphics Vol. 40, No. 4, Article No. 145, 2021.
Crossref Google Scholar
[5]
Butterworth, J. Dance Studies: The Basics. Routledge Press, 2011.
Crossref
[6]
Holden, D.; Saito, J.; Komura, T.; Joyce, T. Learning motion manifolds with convolutional autoencoders. In: Proceedings of the SIGGRAPH Asia 2015 Technical Briefs, Article No. 18, 2015.
Crossref
[7]
Holden, D.; Saito, J.; Komura, T. A deep learning framework for character motion synthesis and editing. ACM Transactions on Graphics Vol. 35, No. 4, Article No. 138, 2016.
Crossref Google Scholar
[8]
Aberman, K.; Weng, Y. J.; Lischinski, D.; Cohen-Or, D.; Chen, B. Q. Unpaired motion style transfer from video to animation. ACM Transactions on Graphics Vol. 39, No. 4, Article No. 64, 2020.
Crossref Google Scholar
[9]
Dong, Y. Z.; Aristidou, A.; Shamir, A.; Mahler, M.; Jain, E. Adult2child: Motion style transfer using CycleGANs. In: Proceedings of the 13th ACM SIGGRAPH Conference on Motion, Interaction and Games, Article No. 13, 2020.
Crossref
[10]
Wen, Y. H.; Yang, Z. P.; Fu, H. B.; Gao, L.; Sun, Y. N.; Liu, Y. J. Autoregressive stylized motion synthesis with generative flow. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13607–13607, 2021.
Crossref
[11]
Koutedakis, Y.; Craig Sharp, N. C. The Fit and Healthy Dancer. Wiley Press, 1999.
[12]
Krasnow, D.; Chatfield, S. J. Development of the “performance competence evaluation measure”: Assessing qualitative aspects of dance performance. Journal of Dance Medicine & Science Vol. 13, No. 4, 101–107, 2009.
Google Scholar
[13]
Neave, N.; McCarty, K.; Freynik, J.; Caplan, N.; Hönekopp, J.; Fink, B. Male dance moves that catch a woman’s eye. Biology Letters Vol. 7, No. 2, 221–224, 2011.
Crossref Google Scholar
[14]
Torrents, C.; Castañer, M.; Jofre, T.; Morey, G.; Reverter, F. Kinematic parameters that influence the aesthetic perception of beauty in contemporary dance. Perception Vol. 42, No. 4, 447–458, 2013.
Crossref Google Scholar
[15]
Park, Y. S. Correlation analysis between dance experience and smoothness of dance movement by using three jerk-based quantitative methods. Korean Journal of Sport Biomechanics Vol. 26, No. 1, 1–9, 2016.
Crossref Google Scholar
[16]
Alexiadis, D. S.; Kelly, P.; Daras, P.; O’Connor, N. E.; Boubekeur, T.; Ben Moussa, M. Evaluating a dancer’s performance using kinect-based skeleton tracking. In: Proceedings of the 19th ACM International Conference on Multimedia, 659–662, 2011.
Crossref
[17]
Raheb, K. E.; Stergiou, M.; Katifori, A.; Ioannidis, Y. Dance interactive learning systems: A study on interaction workflow and teaching approaches. ACM Computing Surveys Vol. 52, No. 3, Article No. 50, 2019.
Crossref Google Scholar
[18]
Chen, H. Y.; Cheng, Y. H.; Lo, A. Improve dancing skills with motion capture systems: Case study of a Taiwanese high school dance class. Research in Dance Education , 2021.
Crossref Google Scholar
[19]
Chan, J. C. P.; Leung, H.; Tang, J. K. T.; Komura, T. A virtual reality dance training system using motion capture technology. IEEE Transactions on Learning Technologies Vol. 4, No. 2, 187–195, 2011.
Crossref Google Scholar
[20]
Aristidou, A.; Stavrakis, E.; Charalambous, P.; Chrysanthou, Y.; Himona, S. L. Folk dance evaluation using laban movement analysis. Journal on Computing and Cultural Heritage Vol. 8, No. 4, Article No. 20, 2015.
Crossref Google Scholar
[21]
Laban, R. The Mastery of Movement, 4th edn. Dance Books Ltd., 2011.
[22]
Tenenbaum, J.; Freeman, W. Separating style and content. In: Proceedings of the Advances in Neural Information Processing Systems, 662–668, 1996.
[23]
Aristidou, A.; Zeng, Q.; Stavrakis, E.; Yin, K. K.; Cohen-Or, D.; Chrysanthou, Y.; Chen, B. Emotion control of unstructured dance movements. In: Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation, Article No. 9, 2017.
Crossref
[24]
Brand, M.; Hertzmann, A. Style machines. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, 183–192, 2000.
Crossref
[25]
Hsu, E.; Pulli, K.; Popović J. Style translation for human motion. ACM Transactions on Graphics Vol. 24, No. 3, 1082–1089, 2005.
Crossref Google Scholar
[26]
Xia, S. H.; Wang, C. Y.; Chai, J. X.; Hodgins, J. Realtime style transfer for unlabeled heterogeneous human motion. ACM Transactions on Graphics Vol. 34, No. 4, Article No. 119, 2015.
Crossref Google Scholar
[27]
Mason, I.; Starke, S.; Zhang, H.; Bilen, H.; Komura, T. Few-shot learning of homogeneous human locomotion styles. Computer Graphics Forum Vol. 37, No. 7, 143–153, 2018.
Crossref Google Scholar
[28]
Smith, H. J.; Cao, C.; Neff, M.; Wang, Y. Y. Efficient neural networks for real-time motion style transfer. Proceedings of the ACM on Computer Graphics and Interactive Techniques Vol. 2, No. 2, Article No. 13, 2019.
Crossref Google Scholar
[29]
Du, H.; Herrmann, E.; Sprenger, J.; Cheema, N.; Hosseini, S.; Fischer, K.; Slusallek, P. Stylisticlocomotion modeling with conditional variational autoencoder. In: Proceedings of the 12th ACM SIGGRAPH Conference on Motion, Interaction and Games, Article No. 32, 2019.
[30]
Vincent, P.; Larochelle, H.; Lajoie, I.; Bengio, Y.; Manzagol, P. A. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. The Journal of Machine Learning Research Vol. 11, 3371–3408, 2010.
Google Scholar
[31]
Gatys, L.; Ecker, A.; Bethge, M. A neural algorithm of artistic style. Journal of Vision Vol. 16, No. 12, 326, 2016.
Crossref Google Scholar
[32]
Huang, X.; Belongie, S. Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision, 1510–1519, 2017.
Crossref
[33]
Arikan, O.; Forsyth, D. A. Interactive motion generation from examples. ACM Transactions on Graphics Vol. 21, No. 3, 483–490, 2002.
Crossref Google Scholar
[34]
Kim, T. H.; Park, S. I.; Shin, S. Y. Rhythmic-motion synthesis based on motion-beat analysis. ACM Transactions on Graphics Vol. 22, No. 3, 392–401, 2003.
Crossref Google Scholar
[35]
Lee, H. C.; Lee, I. K. Automatic synchronization of background music and motion in computer animation. Computer Graphics Forum Vol. 24, No. 3, 353–361, 2005.
Crossref Google Scholar
[36]
Shiratori, T.; Nakazawa, A.; Ikeuchi, K. Dancing-to-music character animation. Computer Graphics Forum Vol. 25, No. 3, 449–458, 2006.
Crossref Google Scholar
[37]
Tang, T. R.; Jia, J.; Mao, H. Y. Dance with melody: An LSTM-autoencoder approach to music-oriented dance synthesis. In: Proceedings of the 26th ACM International Conference on Multimedia, 1598–1606, 2018.
Crossref
[38]
Lee, H. Y.; Yang, X.; Liu, M. Y.; Wang, T. C.; Lu, Y. D.; Yang, M. H.; Kautz, J. Dancing to music. In: Proceedings of the 33rd Conference on Neural Information Processing Systems, 2019.
[39]
Tsuchida, S.; Fukayama, S.; Hamasaki, M.; Goto, M. AIST dance video database: Multi-genre, multi-dancer, and multi-camera database for dance information processing. In: Proceedings of the 20th International Society for Music Information Retrieval Conference, 501–510, 2019.
[40]
Zhuang, W. L.; Wang, C. Y.; Chai, J. X.; Wang, Y. G.; Shao, M.; Xia, S. Y. Music2Dance: DanceNet for music-driven dance generation. ACM Transactions on Multimedia Computing, Communications, and Applications Vol. 18, No. 2, Article No. 65, 2022.
Crossref Google Scholar
[41]
Aristidou, A.; Yiannakidis, A.; Aberman, K.; Cohen-Or, D.; Shamir, A.; Chrysanthou, Y. Rhythm is a dancer: Music-driven motion synthesis with global structure. IEEE Transactions on Visualization and Computer Graphics , 2022.
Crossref Google Scholar
[42]
Tadamura, K.; Nakamae, E. Synchronizing computer graphics animation and audio. IEEE MultiMedia Vol. 5, No. 4, 63–73, 1998.
Crossref Google Scholar
[43]
Cardle, M.; Barthe, L.; Brooks, S.; Robinson, P. Music-driven motion editing: Local motion transformations guided by music analysis. In: Proceedings of the 20th Eurographics UK Conference, 38–44, 2002.
Crossref
[44]
Laichuthai, A.; Kanongchaiyo, P. Synchronization between motion and music using motion graph. In: Proceedings of the 8th Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology, 496–499, 2011.
Crossref
[45]
Davis, A.; Agrawala, M. Visual rhythm and beat. ACM Transactions on Graphics Vol. 37, No. 4, Article No. 122, 2018.
Crossref Google Scholar
[46]
Bellini, R.; Kleiman, Y.; Cohen-Or, D. Dance to the beat: Synchronizing motion to audio. Computational Visual Media Vol. 4, No. 3, 197–208, 2018.
Crossref Google Scholar
[47]
Chung, J. S.; Zisserman, A. Out of time: Automated lip sync in the wild. In: Computer Vision – ACCV 2016 Workshops. Lecture Notes in Computer Science, Vol. 10117. Chen, C. S.; Lu, J.; Ma, K. K. Eds. Springer Cham, 251–263, 2017.
Crossref
[48]
Halperin, T.; Ephrat, A.; Peleg, S. Dynamic temporal alignment of speech to lips. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 3980–3984, 2019.
Crossref
[49]
Wang, J. R.; Fang, Z. Y.; Zhao, H. AlignNet: A unifying approach to audio-visual alignment. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 3298–3306, 2020.
Crossref
[50]
Phillips, G. M. Interpolation and Approximation by Polynomials. New York: Springer, 2003.
Crossref
[51]
Holden, D.; Komura, T.; Saito, J. Phase-func-tioned neural networks for character control. ACM Transactions on Graphics Vol. 36, No. 4, Article No. 42, 2017.
Crossref Google Scholar
[52]
Aristidou, A.; Lasenby, J.; Chrysanthou, Y.; Shamir, A. Inverse kinematics techniques in computer graphics: A survey. Computer Graphics Forum Vol. 37, No. 6, 35–58, 2018.
Crossref Google Scholar
[53]
McFee, B.; Raffel, C.; Liang, D. W.; Ellis, D.; McVicar, M.; Battenberg, E.; Nieto, O. Librosa: Audio and music signal analysis in python. In: Proceedings of the 14th Python in Science Conference, 18–24, 2015.
Crossref
[54]
Sakoe, H.; Chiba, S. Dynamic programming algorithm optimization for spoken word recognition. IEEETransactions on Acoustics, Speech, and Signal Processing Vol. 26, No. 1, 43–49, 1978.
Crossref Google Scholar
[55]
Rabiner, L.; Juang, B. H. Fundamentals of Speech Recognition. Prentice-Hall, Inc., 1993,
[56]
Daugman, J. G. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A Vol. 2, No. 7, 1160–1169, 1985.
Crossref Google Scholar
[57]
Dowson, D. C.; Landau, B. V. The Fréchet distance between multivariate normal distributions. Journal of Multivariate Analysis Vol. 12, No. 3, 450–455, 1982.
Crossref Google Scholar
[58]
Aristidou, A.; Cohen-Or, D.; Hodgins, J. K.; Chrysanthou, Y.; Shamir, A. Deep motifs and motion signatures. ACM Transactions on Graphics Vol. 37, No. 6, Article No. 187, 2018.
Crossref Google Scholar
[59]
Zhou, Y.; Barnes, C.; Lu, J. W.; Yang, J. M.; Li, H. On the continuity of rotation representations in neural networks. In: Proceedings of the IEEE/ CVF Conference on Computer Vision and Pattern Recognition, 5738–5746, 2019.
Crossref
[60]
Andreou, N.; Aristidou, A.; Chrysanthou, Y. Pose representations for deep skeletal animation. In: Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2022.
Crossref
Computational Visual Media
Volume 9 Issue 3,
September 2023
Pages 531-550
DOI: 10.1007/s41095-022-0292-6
Cite this article:
Zhou Q, Li M, Zeng Q, et al. Let’s all dance: Enhancing amateur dance motions. Computational Visual Media, 2023, 9(3): 531-550. https://doi.org/10.1007/s41095-022-0292-6

1078

Views

70

Downloads

3

Crossref

4

Web of Science

4

Scopus

0

CSCD

Altmetrics
Received: 12 February 2022
Accepted: 05 May 2022
Published: 31 March 2023
© The Author(s) 2023.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduc-tion in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.
Return