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Science China Information Sciences

, Volume 57, Issue 7, pp 1–11 | Cite as

Real-time control of human actions using inertial sensors

  • HuaJun Liu
  • FaZhi HeEmail author
  • FuXi Zhu
  • Qing Zhu
Research Paper

Abstract

Our study proposes a new local model to accurately control an avatar using six inertial sensors in real-time. Creating such a system to assist interactive control of a full-body avatar is challenging because control signals from our performance interfaces are usually inadequate to completely determine the whole body movement of human actors. We use a pre-captured motion database to construct a group of local regression models, which are used along with the control signals to synthesize whole body human movement. By synthesizing a variety of human movements based on actors’ control in real-time, this study verifies the effectiveness of the proposed system. Compared with the previous models, our proposed model can synthesize more accurate results. Our system is suitable for common use because it is much cheaper than commercial motion capture systems.

Keywords

avatars motion capture/editing/synthesis interaction techniques game interaction animation simulation 

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Supplementary material

11432_2013_4898_MOESM1_ESM.pdf (66 kb)
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References

  1. 1.
    Badler N I, Hollick M, Granieri J. Realtime control of a virtual human using minimal sensors. Presence, 1993, 2: 82–86Google Scholar
  2. 2.
    Semwal S, Hightower R, Stansfield S. Mapping algorithms for real-time control of an avatar using eight sensors. Presence, 1998, 7: 1–21CrossRefGoogle Scholar
  3. 3.
    Yin K, Pai D K. FootSee: an interactive animation system. In: Proceedings of the 2003 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, San Diego, 2003. 329–338Google Scholar
  4. 4.
    Slyper R, Hodgins J. Action capture with accelerometers. In: Proceedings of 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Dublin, 2008. 193–199Google Scholar
  5. 5.
    Ha S, Bai Y, Liu C. Human motion reconstruction from force sensors. In: Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Vancouver, 2011. 129–138CrossRefGoogle Scholar
  6. 6.
    Tautges J, Zinke A, Kruger B, et al. Motion reconstruction using sparse accelerometer data. ACM Trans Graph, 2011, 30: 18CrossRefGoogle Scholar
  7. 7.
    Liu H J, Wei X L, Chai J X, et al. Realtime human motion control with a small number of inertial sensors. In: Proceedings of the 2011 Symposium on Interactive 3D Graphics and Games. New York: ACM, 2011. 133–140Google Scholar
  8. 8.
    Shotton J, Fitzgibbon A, Cook M, et al. Real-time human pose recognition in parts from single depth images. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. Washington DC: IEEE Computer Society, 2011. 1297–1304Google Scholar
  9. 9.
    Wei X L, Zhang P Z, Chai J X. Accurate realtime full-body motion capture using a single depth camera. ACM Trans Graph, 2012, 31: 188CrossRefGoogle Scholar
  10. 10.
    Kovar L, Gleicher M. Automated extraction and parameterization of motions in large data sets. ACM Trans Graph, 2004, 23: 559–568CrossRefGoogle Scholar
  11. 11.
    Kwon T, Shin S Y. Motion modeling for online locomotion synthesis. In: ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Los Angeles, 2005. 29–38Google Scholar
  12. 12.
    Mukai T, Kuriyama S. Geostatistical motion interpolation. ACM Trans Graph, 2005, 24: 1062–1070CrossRefGoogle Scholar
  13. 13.
    Heck R, Gleicher M. Parametric motion graphs. In: Proceedings of the 2007 Symposium on Interactive 3D Graphics and Games. New York: ACM, 2007. 129–136CrossRefGoogle Scholar
  14. 14.
    Kovar L, Gleicher M, Pighin F. Motion graphs. ACM Trans Graph, 2002, 21: 473–482CrossRefGoogle Scholar
  15. 15.
    Lee Y, Wampler K, Bernstein G, et al. Motion fields for interactive character locomotion. ACM Trans Graph, 2010, 29: 1–8Google Scholar
  16. 16.
    Levine S, Wang J, Haraux A, et al. Continuous character control with low-dimensional embeddings. ACM Trans Graph, 2012, 31: 28CrossRefGoogle Scholar
  17. 17.
    Min J Y, Chai J X. Motion graphs++: a compact generative model for semantic motion analysis and synthesis. ACM Trans Graph, 2012, 31: 153CrossRefGoogle Scholar
  18. 18.
    Safonova A, Hodgins J K. Construction and optimal search of interpolated motion graphs. ACM Trans Graph, 2007, 26: 108CrossRefGoogle Scholar
  19. 19.
    Chai J X, Hodgins J. Performance animation from low-dimensional control signals. ACM Trans Graph, 2005, 24: 686–696CrossRefGoogle Scholar
  20. 20.
    Grochow K, Martin S L, Hertzmann A, et al. Style-based inverse kinematics. ACM Trans Graph, 2004, 23: 522–531CrossRefGoogle Scholar
  21. 21.
    Lau M, Chai J X, Xu Y Q, et al. Face poser: interactive modeling of 3D facial expressions using facial priors. ACM Trans Graph, 2009, 29: 3CrossRefGoogle Scholar
  22. 22.
    Liu H J, He F Z, Cai X T, et al. Performance-based control interfaces using mixture of factor analyzers. Visual Comput, 2011, 27: 595–603CrossRefzbMATHGoogle Scholar
  23. 23.
    Min J Y, Chen Y L, Chai J X. Interactive generation of human animation with deformable motion models. ACM Trans Graph, 2009, 29: 9CrossRefGoogle Scholar
  24. 24.
    Wei X L, Min J Y, Chai J X. Physically valid statistical models for human motion generation. ACM Trans Graph, 2011, 30: 19Google Scholar
  25. 25.
    Ye Y, Liu C. Synthesis of responsive motion using a dynamic model. Comput Graph Forum, 2010, 29: 555–562CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of ComputerWuhan UniversityWuhanChina
  2. 2.School of Geosciences and Environmental EngineeringSouthwest Jiaotong UniversityChengduChina

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