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Optimal and interactive keyframe selection for motion capture

  • Richard RobertsEmail author
  • J. P. Lewis
  • Ken Anjyo
  • Jaewoo Seo
  • Yeongho Seol
Open Access
Research Article
  • 98 Downloads

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.

Keywords

motion capture motion editing keyframe animation dynamic programming 

Notes

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.

Supplementary material

Supplementary material, approximately 145 MB.

References

  1. [1]
    Lam, D. Personal communication. Electronic Arts, 2017.Google Scholar
  2. [2]
    Shelton, D. Personal communication. Electronic Arts, 2017.Google Scholar
  3. [3]
    White, T. Animation from Pencils to Pixels: Classical Techniques for Digital Animators. Burlington: Focal Press, 2006.Google Scholar
  4. [4]
    Roy, K. How to Cheat in Maya 2014: Tools and Techniques for Character Animation. Burlington: Focal Press, 2013.CrossRefGoogle Scholar
  5. [5]
    Ramer, U. An iterative procedure for the polygonal approximation of plane curves. Computer Graphics and Image Processing Vol. 1, No. 3, 244–256, 1972.CrossRefGoogle Scholar
  6. [6]
    Bertsekas, D. P. Network Optimization: Continuous and Discrete Models. Belmont: Athena Scientific, 1998.zbMATHGoogle Scholar
  7. [7]
    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.
  8. [8]
    The games industry in numbers. 2018. Available at https://https://ukie.org.uk.
  9. [9]
    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.Google Scholar
  10. [10]
    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.CrossRefGoogle Scholar
  11. [11]
    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.Google Scholar
  12. [12]
    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.CrossRefGoogle Scholar
  13. [13]
    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.Google Scholar
  14. [14]
    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.Google Scholar
  15. [15]
    Bulut, E.; Capin, T. Keyframe extraction from motion capture data by curve saliency. Available at http://www.people.vcu.edu/∼ebulut/casa.pdf.
  16. [16]
    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.CrossRefGoogle Scholar
  17. [17]
    Marr, D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York: Henry Holt and Co., Inc., 1982.Google Scholar
  18. [18]
    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.CrossRefGoogle Scholar
  19. [19]
    Lowe, D. G. Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence Vol. 31, No. 3, 355–395, 1987.CrossRefGoogle Scholar
  20. [20]
    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.Google Scholar
  21. [21]
    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.Google Scholar
  22. 22]
    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.CrossRefGoogle Scholar
  23. [23]
    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.CrossRefGoogle Scholar
  24. [24]
    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.Google Scholar
  25. [25]
    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.Google Scholar
  26. [26]
    Bellman, R.; Kashef, B.; Vasudevan, R. Splines via dynamic programming. Journal of Mathematical Analysis and Applications Vol. 38, No. 2, 471–479, 1972.MathSciNetCrossRefzbMATHGoogle Scholar
  27. [27]
    Lewis, J. P.; Anjyo, K. Identifying salient points. In: Proceedings of the ACM SIGGRAPH ASIA 2009 Sketches, Article No. 41, 2009.Google Scholar
  28. [28]
    Witkin, A.; Popovic, Z. Motion warping. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, 105–108, 1995.Google Scholar
  29. [29]
    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.Google Scholar
  30. [30]
    Witkin, A.; Kass, M. Spacetime constraints. ACM SIGGRAPH Computer Graphics Vol. 22, No. 4, 159–168, 1988.CrossRefGoogle Scholar
  31. [31]
    Gleicher, M. Animation from observation: Motion capture and motion editing. ACM SIGGRAPH Computer Graphics Vol. 33, No. 4, 51–54, 1999.CrossRefGoogle Scholar
  32. [32]
    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.Google Scholar
  33. [33]
    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.Google Scholar
  34. [34]
    Kovar, L.; Gleicher, M.; Pighin, F. Motion graphs. ACM Transactions on Graphics Vol. 21, No. 3, 473–482, 2002.CrossRefGoogle Scholar
  35. [35]
    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.Google Scholar
  36. [36]
    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.Google Scholar
  37. [37]
    Park, M. J.; Shin, S. Y. Example-based motion cloning. Computer Animation and Virtual Worlds Vol. 15, Nos. 3–4, 245–257, 2004.CrossRefGoogle Scholar
  38. [38]
    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.Google Scholar
  39. [39]
    Assa, J.; Caspi, Y.; Cohen-Or, D. Action synopsis: Pose selection and illustration. ACM Transactions on Graphics Vol. 24, No. 3, 667–676, 2005.CrossRefGoogle Scholar
  40. [40]
    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.CrossRefGoogle Scholar
  41. [41]
    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.Google Scholar
  42. [42]
    Arikan, O. Compression of motion capture databases. ACM Transactions on Graphics Vol. 25, No. 3, 890–897, 2006.CrossRefzbMATHGoogle Scholar
  43. [43]
    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.CrossRefGoogle Scholar
  44. [44]
    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.CrossRefGoogle Scholar
  45. [45]
    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.CrossRefGoogle Scholar
  46. [46]
    Dijkstra, E. W. A note on two problems in connexion with graphs. Numerische Mathematik Vol. 1, No. 1, 269–271, 1959.MathSciNetCrossRefzbMATHGoogle Scholar
  47. [47]
    Schneider, P. J. An algorithm for automatically fitting digitized curves. In: Graphics Gems. San Diego: Academic Press Professional, Inc., 612–626, 1990.CrossRefGoogle Scholar
  48. [48]
    Adobe Mixamo. 2018. Available at https://www.mixamo.com.
  49. [49]
    Kaufman, J. C.; Simonton, D. K. The Social Science of Cinema. Oxford University Press, 2013.Google Scholar
  50. [50]
    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.CrossRefGoogle Scholar
  51. [51]
    Lasseter, J. Principles of traditional animation applied to 3D computer animation. ACM SIGGRAPH Computer Graphics Vol. 21, No. 4, 35–44, 1987.CrossRefGoogle Scholar

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© The Author(s) 2019

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Authors and Affiliations

  • Richard Roberts
    • 1
    Email author
  • J. P. Lewis
    • 2
  • Ken Anjyo
    • 1
    • 3
  • Jaewoo Seo
    • 4
  • Yeongho Seol
    • 5
  1. 1.Victoria University of WellingtonWellingtonNew Zealand
  2. 2.SEED, Electronic ArtsLos AngelesUSA
  3. 3.OLM DigitalTokyoJapan
  4. 4.PinscreenLos AngelesUSA
  5. 5.Weta DigitalWellingtonNew Zealand

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