Abstract
As social media has matured, uploading video content has increased. Multiple videos of physical performances, such as dance, are difficult to integrate into high-quality videos without knowledge of video-editing principles. In this study, we present a system that automatically edits dance-performance videos taken from multiple viewpoints into a more attractive and sophisticated dance video. Our system can crop the frame of each camera appropriately by using the performer’s behavior and skeleton information. The system determines the camera switches and cut lengths following a probabilistic model of general cinematography guidelines and of knowledge extracted from expert experience. In this study, our system automatically edited a dance video of four performers taken from multiple viewpoints, and ten video-production experts evaluated the generated video. As a result of a comparison of another automatic editing system, our system tended to be performed better.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Heck, R., Wallick, M., Gleicher, M.: Virtual videography. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 3(1) (2007). https://doi.org/10.1145/1198302.1198306. Article No. 4
Arev, I., Park, H.S., Sheikh, Y., Hodgins, J., Shamir, A.: Automatic editing of footage from multiple social cameras. ACM Trans. Graph. (TOG) 33(4) (2014). https://doi.org/10.1145/2601097.2601198. Article No. 81
Ranjan, A., Birnholtz, J., Balakrishnan, R.: Improving meeting capture by applying television production principles with audio and motion detection. In: Proceedings of CHI 2008, pp. 227–236. ACM, New York (2008). https://doi.org/10.1145/1357054.1357095
Zsombori, V., Frantzis, M., Guimaraes, R.L., Ursu, M.F., Cesar, P., Kegel, I., Craigie, R., Bulterman, D.C.: Automatic generation of video narratives from shared UGC. In: Proceedings of Hypertext 2011, pp. 325–334. ACM, New York (2011). https://doi.org/10.1145/1995966.1996009
Lu, Z., Grauman, K.: Story-driven summarization for egocentric video. In: Proceedings of CVPR 2013, pp. 2714–2721. IEEE, New York (2013). https://doi.org/10.1109/CVPR.2013.350
Jain, E., Sheikh, Y., Shamir, A., Hodgins, J.: Gaze-driven video re-editing. ACM Trans. Graph. (TOG) 34(2) (2015). https://doi.org/10.1145/2699644. Article No. 21
Shin, H.V., Berthouzoz, F., Li, W., Durand, F.: Visual transcripts: lecture notes from blackboard-style lecture videos. ACM Trans. Graph. (TOG) 34(6) (2015). https://doi.org/10.1145/2816795.2818123. Article No. 240
Kumar, M., Gandhi, V., Ronfard, R., Gleicher, M.: Zooming on all actors: automatic focus + context split screen video generation. Comput. Graph. Forum 36, 455–465 (2017). https://doi.org/10.2312/wiced.20171070. Article No. 2
Sun, X., Foote, J., Kimber, D., Manjunath, B.S.: Region of interest extraction and virtual camera control based on panoramic video capturing. IEEE Trans. Multimed. 7(5), 981–990 (2005). https://doi.org/10.1109/TMM.2005.854388
Roininen, M.J., Leppnen, J., Eronen, A.J., Curcio, I.D., Gabbouj, M.: Modeling the timing of cuts in automatic editing of concert videos. Multimed. Tools Appl. 76(5), 6683–6707 (2017). https://doi.org/10.1007/s11042-016-3304-7
Mate, S., Curcio, I.D.: Automatic video remixing systems. IEEE Commun. Mag. 55(1), 180–187 (2017). https://doi.org/10.1109/MCOM.2017.1500493CM
Truong, A., Berthouzoz, F., Li, W., Agrawala, M.: QuickCut: an interactive tool for editing narrated video. In: Proceedings of UIST 2016, pp. 497–507. ACM, New York (2016). https://doi.org/10.1145/2984511.2984569
Leake, M., Davis, A., Truong, A., Agrawala, M.: Computational video editing for dialogue-driven scenes. ACM Trans. Graph. (TOG) 36(4) (2017). https://doi.org/10.1145/3072959.3073653. Article No. 130
Jeong, K.A., Suk, H.J.: Jockey time: making video playback to enhance emotional effect. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 62–66. ACM, New York (2016). https://doi.org/10.1145/2964284.2967183
Nam, T.J., Lee, J.H., Park, S., Suk, H.J.: Understanding the relation between emotion and physical movements. Int. J. Affect. Eng. 13, 217–226 (2014). https://doi.org/10.5057/ijae.13.217. Article No. 3
Montepare, J.M., Goldstein, S.B., Clausen, A.: The identification of emotions from gait information. J. Nonverbal Behav. 11(1), 33–42 (1987). https://doi.org/10.1007/BF00999605
Foust, J.C., Fink, E.J., Gross, L.S.: Video Production: Disciplines and Techniques. Taylor and Francis, Abingdon (2012)
Bowen, C.J.: Grammar of the Edit. Taylor and Francis, Abingdon (2013)
Böck, S., Krebs, F., Widmer, G.: Joint beat and downbeat tracking with recurrent neural networks. In: ISMIR, pp. 255–261 (2016)
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: CVPR (2017)
Farneback, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45103-X_50
Acknowledgments
This work was supported in part by JST ACCEL Grant Number JPMJAC1602, Japan.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Tsuchida, S., Fukayama, S., Goto, M. (2018). Automatic System for Editing Dance Videos Recorded Using Multiple Cameras. In: Cheok, A., Inami, M., Romão, T. (eds) Advances in Computer Entertainment Technology. ACE 2017. Lecture Notes in Computer Science(), vol 10714. Springer, Cham. https://doi.org/10.1007/978-3-319-76270-8_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-76270-8_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76269-2
Online ISBN: 978-3-319-76270-8
eBook Packages: Computer ScienceComputer Science (R0)