Abstract
Motion capture is increasingly used in games and movies, but often requires editing before it can be used, for many reasons. The motion may need to be adjusted to correctly interact with virtual objects or to fix problems that result from mapping the motion to a character of a different size or, beyond such technical requirements, directors can request stylistic changes. Unfortunately, editing is laborious because of the low-level representation of the data. While existing motion editing methods accomplish modest changes, larger edits can require the artist to “re-animate” the motion by manually selecting a subset of the frames as keyframes. In this paper, we automatically find sets of frames to serve as keyframes for editing the motion. We formulate the problem of selecting an optimal set of keyframes as a shortest-path problem, and solve it efficiently using dynamic programming. We create a new simplified animation by interpolating the found keyframes using a naive curve fitting technique. Our algorithm can simplify motion capture to around 10% of the original number of frames while retaining most of its detail. By simplifying animation with our algorithm, we realize a new approach to motion editing and stylization founded on the time-tested keyframe interface. We present results that show our algorithm outperforms both research algorithms and a leading commercial tool.
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References
Lam, D. Personal communication. Electronic Arts, 2017.
Shelton, D. Personal communication. Electronic Arts, 2017.
White, T. Animation from Pencils to Pixels: Classical Techniques for Digital Animators. Burlington: Focal Press, 2006.
Roy, K. How to Cheat in Maya 2014: Tools and Techniques for Character Animation. Burlington: Focal Press, 2013.
Ramer, U. An iterative procedure for the polygonal approximation of plane curves. Computer Graphics and Image Processing Vol. 1, No. 3, 244–256, 1972.
Bertsekas, D. P. Network Optimization: Continuous and Discrete Models. Belmont: Athena Scientific, 1998.
The U.S. game industry has 2,457 companies supporting 220,000 jobs. 2018. Available at https://venturebeat.com/2017/02/14/the-u-s-game-industry-has-2457-companies-supporting-220000-jobs.
The games industry in numbers. 2018. Available at https://https://ukie.org.uk.
Wang, X.; Chen, Q.; Wang, W. 3D human motion editing and synthesis: A survey. Computational and Mathematical Methods in Medicine Vol. 2014, Article ID 104535, 2014.
Miura, T.; Kaiga, T.; Shibata, T.; Katsura, H.; Tajima, K.; Tamamoto, H. A hybrid approach to keyframe extraction from motion capture data using curve simplification and principal component analysis. IEEJ Transactions on Electrical and Electronic Engineering Vol. 9, No. 6, 697–699, 2014.
Wolin, A.; Eoff, B.; Hammond, T. ShortStraw: A simple and effective corner finder for polylines. In: Proceedings of the 5th Eurographics Conference on Sketch-based Interfaces and Modeling, 33–40, 2008.
So, C. K. F.; Baciu, G. Entropy-based motion extraction for motion capture animation. Computer Animation and Virtual Worlds Vol. 16, Nos. 3–4, 225–235, 2005.
Cuntoor, N. P.; Chellappa, R. Key frame-based activity representation using antieigenvalues. In: Computer Vision — ACCV 2006. Lecture Notes in Computer Science, Vol. 3852. Narayanan, P. J.; Nayar, S. K.; Shum, H. Y. Eds. Springer Berlin Heidelberg, 499–508, 2006.
Wei, X. P.; Liu, R.; Zhang, Q. Key-frame extraction of human motion capture data based on least-square distance curve. Journal of Convergence Information Technology Vol. 7, 11–19, 2012.
Bulut, E.; Capin, T. Keyframe extraction from motion capture data by curve saliency. Available at http://www.people.vcu.edu/∼ebulut/casa.pdf.
Halit, C.; Capin, T. Multiscale motion saliency for keyframe extraction from motion capture sequences. Computer Animation and Virtual Worlds Vol. 22, No. 1, 3–14, 2011.
Marr, D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York: Henry Holt and Co., Inc., 1982.
Douglas, D.; Peucker, T. K. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization Vol. 10, No. 2, 112–122, 1973.
Lowe, D. G. Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence Vol. 31, No. 3, 355–395, 1987.
Lim, I. S.; Thalmann, D. Key-posture extraction out of human motion data. In: Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 2, 1167–1169, 2001.
Seol, Y.; Seo, J.; Kim, P. H.; Lewis, J. P.; Noh, J. Artist friendly facial animation retargeting. ACM Transactions on Graphics Vol. 30, No. 6, Article No. 162, 2011.
Liu, X.-M.; Hao, A.-M.; Zhao, D. Optimization-based key frame extraction for motion capture animation. The Visual Computer Vol. 29, No. 1, 85–95, 2013.
Zhang, Q.; Zhang, S.; Zhou, D. Keyframe extraction from human motion capture data based on a multiple population genetic algorithm. Symmetry Vol. 6, No. 4, 926–937, 2014.
Zhang, Y.; Cao, J. 3D human motion key-frames extraction based on asynchronous learningfactor PSO. In: Proceedings of the 5th International Conference on Instrumentation and Measurement, Computer, Communication and Control, 1617–1620, 2015.
Chang, X.; Yi, P.; Zhang, Q. Key frames extraction from human motion capture data based on hybrid particle swarm optimization algorithm. In: Recent Developments in Intelligent Information and Database Systems. Studies in Computational Intelligence, Vol. 642. Król, D.; Madeyski, L.; Nguyen, N. Eds. Springer Cham, 335–342, 2016.
Bellman, R.; Kashef, B.; Vasudevan, R. Splines via dynamic programming. Journal of Mathematical Analysis and Applications Vol. 38, No. 2, 471–479, 1972.
Lewis, J. P.; Anjyo, K. Identifying salient points. In: Proceedings of the ACM SIGGRAPH ASIA 2009 Sketches, Article No. 41, 2009.
Witkin, A.; Popovic, Z. Motion warping. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, 105–108, 1995.
Lee, J.; Shin, S. Y. A hierarchical approach to interactive motion editing for human-like figures. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, 39–48, 1999.
Witkin, A.; Kass, M. Spacetime constraints. ACM SIGGRAPH Computer Graphics Vol. 22, No. 4, 159–168, 1988.
Gleicher, M. Animation from observation: Motion capture and motion editing. ACM SIGGRAPH Computer Graphics Vol. 33, No. 4, 51–54, 1999.
Guay, M.; Cani, M.-P.; Ronfard, R. The line of action: An intuitive interface for expressive character posing. ACM Transactions on Graphics Vol. 32, No. 6, Article No. 205, 2013.
Choi, B.; i Ribera, R. B.; Lewis, J. P.; Seol, Y.; Hong, S.; Eom, H.; Jung, S.; Noh, J. SketchiMo: Sketch-based motion editing for articulated characters. ACM Transactions on Graphics Vol. 35, No. 4, Article No. 146, 2016.
Kovar, L.; Gleicher, M.; Pighin, F. Motion graphs. ACM Transactions on Graphics Vol. 21, No. 3, 473–482, 2002.
Casas, D.; Tejera, M.; Guillemaut, J.-Y.; Hilton, A. 4D parametric motion graphs for interactive animation. In: Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, 103–110, 2012.
Huang, P.; Tejera, M.; Collomosse, J.; Hilton, A. Hybrid skeletal-surface motion graphs for character animation from 4D performance capture. ACM Transactions on Graphics Vol. 34, No. 2, Article No. 17, 2015.
Park, M. J.; Shin, S. Y. Example-based motion cloning. Computer Animation and Virtual Worlds Vol. 15, Nos. 3–4, 245–257, 2004.
Peng, X. B.; Abbeel, P.; Levine, S.; van de Panne, M. DeepMimic: Example-guided deep reinforcement learning of physics-based character skills. ACM Transactions on Graphics Vol. 37, No.4, Article No. 143, 2018.
Assa, J.; Caspi, Y.; Cohen-Or, D. Action synopsis: Pose selection and illustration. ACM Transactions on Graphics Vol. 24, No. 3, 667–676, 2005.
Yasuda, H.; Kaihara, R.; Saito, S.; Nakajima, M. Motion belts: Visualization of human motion data on a timeline. IEICE Transactions on Information and Systems Vol. E91-D, No. 4, 1159–1167, 2008.
Hu, Y.; Wu, S.; Xia, S.; Fu, J.; Chen, W. Motion track: Visualizing variations of human motion data. In: Proceedings of the IEEE Pacific Visualization Symposium, 153–160, 2010.
Arikan, O. Compression of motion capture databases. ACM Transactions on Graphics Vol. 25, No. 3, 890–897, 2006.
Huang, K.-S.; Chang, C.-F.; Hsu, Y.-Y.; Yang, S.-N. Key probe: A technique for animation keyframe extraction. The Visual Computer Vol. 21, No. 8, 532–541, 2005.
Tournier, M.; Wu, X.; Courty, N.; Arnaud, E.; Reveret, L. Motion compression using principal geodesics analysis. Computer Graphics Forum Vol. 28, No. 2, 355–364, 2009.
Xia, G.; Sun, H.; Niu, X.; Zhang, G.; Feng, L. Keyframe extraction for human motion capture data based on joint kernel sparse representation. IEEE Transactions on Industrial Electronics Vol. 64, No. 2, 1589–1599, 2016.
Dijkstra, E. W. A note on two problems in connexion with graphs. Numerische Mathematik Vol. 1, No. 1, 269–271, 1959.
Schneider, P. J. An algorithm for automatically fitting digitized curves. In: Graphics Gems. San Diego: Academic Press Professional, Inc., 612–626, 1990.
Adobe Mixamo. 2018. Available at https://www.mixamo.com.
Kaufman, J. C.; Simonton, D. K. The Social Science of Cinema. Oxford University Press, 2013.
Miura, T.; Kaiga, T.; Katsura, H.; Tajima, K.; Shibata, T.; Tamamoto, H. Adaptive keypose extraction from motion capture data. Journal of Information Processing Vol. 22, No. 1, 67–75, 2014.
Lasseter, J. Principles of traditional animation applied to 3D computer animation. ACM SIGGRAPH Computer Graphics Vol. 21, No. 4, 35–44, 1987.
Acknowledgements
Many researchers and artists have contributed important insights to this research. The authors would like to give special thanks to Ayumi Kimura and other staff of OLM Digital, to Johan Andersson, Ida Winterhaven, and Binh Le of SEED, Electronic Arts, and also to Ian Loh and other staff of Victoria University of Wellington’s Computational Media Innovation Centre and Virtual Worlds Lab. The authors would also like to thank the Moveshelf team for supporting the web-based presentation of our results.
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Richard Roberts researches into artist-directed tools for animation and visual effects work. Roberts is a currently a research fellow, developing a facial mocap and animation pipeline for the production of a VR narrative experience. He has worked briefly in industry, receiving credit in the Adventures of Tintin, and also has a background developing virtual machines for high-level programming languages.
J. P. Lewis is a numerical programmer and researcher. He is principal research scientist at SEED, the new research lab of Electronic Arts, and is an adjunct associate professor in the machine learning group of Victoria University of Wellington. His interests include computer vision and machine learning applications in entertainment. He has received credits on a few movies including Avatar and the Matrix sequels, and several of his algorithms have been adopted in commercial software including Maya and MATLAB.
Ken Anjyo set up and headed the research and development division of OLM Digital, the digital production company in Tokyo famous for the Pokémon movies and other 3D animated feature films. He became the company’s CTO and is now its executive R&D adviser. He is a board member of VFX-JAPAN, the Japanese association of domestic digital production companies, and a member of the Visual Effects Society. Since 2018, he has also been working as the director of the Computational Media Innovation Center at Victoria University of Wellington.
Jaewoo Seo is a director of R&D at Pinscreen. His research interests include facial animation, motion capture, and GPU programming. Before joining Pinscreen, he was in the visual effects industry as an R&D engineer at ILM, Weta Digital, and OLM Digital. He received his Ph.D. and M.S. degrees in culture technology from KAIST and B.S. degree in digital media and in computer and information engineering from Ajou University.
Yeongho Seol is interested in developing fundamental computer graphics and vision technologies and making them useful in real-world VFX and animation production. He is experienced in a range of motion capture related technologies and works as senior motion capture developer in Weta digital.
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Roberts, R., Lewis, J.P., Anjyo, K. et al. Optimal and interactive keyframe selection for motion capture. Comp. Visual Media 5, 171–191 (2019). https://doi.org/10.1007/s41095-019-0138-z
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DOI: https://doi.org/10.1007/s41095-019-0138-z