Journal of Computer Science and Technology

, Volume 32, Issue 3, pp 536–554 | Cite as

A Survey on Human Performance Capture and Animation

  • Shihong Xia
  • Lin Gao
  • Yu-Kun Lai
  • Ming-Ze Yuan
  • Jinxiang Chai
Survey

Abstract

With the rapid development of computing technology, three-dimensional (3D) human body models and their dynamic motions are widely used in the digital entertainment industry. Human performance mainly involves human body shapes and motions. Key research problems in human performance animation include how to capture and analyze static geometric appearance and dynamic movement of human bodies, and how to simulate human body motions with physical effects. In this survey, according to the main research directions of human body performance capture and animation, we summarize recent advances in key research topics, namely human body surface reconstruction, motion capture and synthesis, as well as physics-based motion simulation, and further discuss future research problems and directions. We hope this will be helpful for readers to have a comprehensive understanding of human performance capture and animation.

Keywords

human surface reconstruction body motion capture motion synthesis physics-based motion simulation 

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References

  1. [1]
    Robinette K M, Daanen H, Paquet E. The CAESAR project: A 3D surface anthropometry survey. In Proc. the 2nd International Conference on 3-D Digital Imaging and Modeling, Oct. 1999, pp.380-386.Google Scholar
  2. [2]
    Wang C C, Chang T K, Yuen M M. From laser-scanned data to feature human model: A system based on fuzzy logic concept. Computer-Aided Design, 2003, 35(3): 241-253.CrossRefGoogle Scholar
  3. [3]
    Woodham R J. Shape from shading. In Shape from Shading, Horm B K P, Brooks M J (eds.), Cambridge, USA: MIT Press, 1989, pp.513-531.Google Scholar
  4. [4]
    Vlasic D, Peers P, Baran I, Debevec P, Popović J, Rusinkiewicz S, Matusik W. Dynamic shape capture using multi-view photometric stereo. ACM Transactions on Graphics, 2009, 28(5): Article No. 174.Google Scholar
  5. [5]
    Wu C, Varanasi K, Liu Y, Seidel H P, Theobalt C. Shading-based dynamic shape refinement from multi-view video under general illumination. In Proc. the IEEE International Conference on Computer Vision, Nov. 2011, pp.1108-1115.Google Scholar
  6. [6]
    Stoll C, Gall J, de Aguiar E, Thrun S, Theobalt C. Video-based reconstruction of animatable human characters. ACM Transactions on Graphics, 2010, 29(6): Article No. 139.Google Scholar
  7. [7]
    Zhu H, Liu Y, Fan J, Dai Q, Cao X. Video-based outdoor human reconstruction. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 27(4): 760-770.CrossRefGoogle Scholar
  8. [8]
    Newcombe R A, Izadi S, Hilliges O, Molyneaux D, Kim D, Davison A J, Kohi P, Shotton J, Hodges S, Fitzgibbon A. KinectFusion: Real-time dense surface mapping and tracking. In Proc. IEEE International Symposium on Mixed and Augmented Reality, Oct. 2011, pp.127-136.Google Scholar
  9. [9]
    Li H, Vouga E, Gudym A, Luo L, Barron J T, Gusev G. 3D self-portraits. ACM Transactions on Graphics, 2013, 32(6): Article No. 187.Google Scholar
  10. [10]
    Newcombe R A, Fox D, Seitz S M. DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In Proc. Conference on Computer Vision and Pattern Recognition, Jun. 2015, pp.343-352.Google Scholar
  11. [11]
    Dou M, Taylor J, Fuchs H, Fitzgibbon A, Izadi S. 3D scanning deformable objects with a single RGB-D sensor. In Proc. Conference on Computer Vision and Pattern Recognition, Jun. 2015, pp.493-501.Google Scholar
  12. [12]
    Butler D A, Izadi S, Hilliges O, Molyneaux D, Hodges S, Kim D. Shake‘n’sense: Reducing interference for overlapping structured light depth cameras. In Proc. the ACM Annual Conference on Human Factors in Computing Systems, May 2012, pp.1933-1936.Google Scholar
  13. [13]
    Tong J, Zhou J, Liu L, Pan Z, Yan H. Scanning 3D full human bodies using kinects. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(4): 643-650.CrossRefGoogle Scholar
  14. [14]
    Lin S, Chen Y, Lai Y K, Martin R R, Cheng Z Q. Fast capture of textured fullbody avatar with RGB-D cameras. The Visual Computer, 2016, 32(6/7/8): 681-691.Google Scholar
  15. [15]
    Ye G, Liu Y, Hasler N, Ji X, Dai Q, Theobalt C. Performance capture of interacting characters with handheld kinects. In Proc. the 12th European Conference on Computer Vision, Volume Part II, Oct. 2012, pp.828-841.Google Scholar
  16. [16]
    Wang C, Liu Y, Guo X, Zhong Z, Le B, Deng Z. Spectral animation compression. Journal of Computer Science and Technology, 2015, 30(3): 540-552.CrossRefGoogle Scholar
  17. [17]
    Anguelov D, Srinivasan P, Koller D, Thrun S, Rodgers J, Davis J. SCAPE: Shape completion and animation of people. ACM Transactions on Graphics, 2005, 24(3): 408-416.CrossRefGoogle Scholar
  18. [18]
    Weiss A, Hirshberg D, Black M J. Home 3D body scans from noisy image and range data. In Proc. the IEEE International Conference on Computer Vision, Nov. 2011, pp. 1951-1958.Google Scholar
  19. [19]
    Bogo F, Black M J, Loper M, Romero J. Detailed full-body reconstructions of moving people from monocular RGB-D sequences. In Proc. the IEEE International Conference on Computer Vision, Dec. 2015, pp.2300-2308.Google Scholar
  20. [20]
    Chen Y, Liu Z, Zhang Z. Tensor-based human body modeling. In Proc. Conference on Computer Vision and Pattern Recognition, Jun. 2013, pp.105-112.Google Scholar
  21. [21]
    Hasler N, Stoll C, Sunkel M, Rosenhahn B, Seidel H P. A statistical model of human pose and body shape. Computer Graphics Forum, 2009, 28(2): 337-346.CrossRefGoogle Scholar
  22. [22]
    Cheng K L, Tong R F, Tang M, Qian J Y, Sarkis M. Parametric human body reconstruction based on sparse key points. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(11): 2467-2479.CrossRefGoogle Scholar
  23. [23]
    Zeng M, Zheng J, Cheng X, Liu X. Templateless quasi-rigid shape modeling with implicit loop-closure. In Proc. the Conference on Computer Vision and Pattern Recognition, Jun. 2013, pp.145-152.Google Scholar
  24. [24]
    Chen Y, Cheng Z Q, Lai C, Martin R R, Dang G. Real-time reconstruction of an animating human body from a single depth camera. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(8): 2000-2011.CrossRefGoogle Scholar
  25. [25]
    Guan P, Reiss L, Hirshberg D A, Weiss A, Black M J. DRAPE: DRessing any PErson. ACM Transactions on Graphics, 2012, 31(4): Article No. 35.Google Scholar
  26. [26]
    Tsoli A, Mahmood N, Black M J. Breathing life into shape: Capturing, modeling and animating 3D human breathing. ACM Transactions on Graphics, 2014, 33(4): Article No. 52.Google Scholar
  27. [27]
    Zheng J, Zeng M, Cheng X, Liu X. Scape-based human performance reconstruction. Computers & Graphics, 2014, 38: 191-198.CrossRefGoogle Scholar
  28. [28]
    Ye M, Wang H, Deng N, Yang X, Yang R. Real-time human pose and shape estimation for virtual try-on using a single commodity depth camera. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(4): 550-559.Google Scholar
  29. [29]
    Pons-Moll G, Romero J, Mahmood N, Black M J. Dyna: A model of dynamic human shape in motion. ACM Transactions on Graphics, 2015, 34(4): Article No. 120.Google Scholar
  30. [30]
    Zuffi S, Black M J. The stitched puppet: A graphical model of 3D human shape and pose. In Proc. Conference on Computer Vision and Pattern Recognition, Jun. 2015.Google Scholar
  31. [31]
    Bogo F, Romero J, Loper M, Black M J. FAUST: Dataset and evaluation for 3D mesh registration. In Proc. Conference on Computer Vision and Pattern Recognition, Jun. 2014, pp.3794-3801.Google Scholar
  32. [32]
    Bogo F, Romero J, Pons-Moll G, Black M. Dynamic faust: Registering human bodies in motion. In Proc. the Conference on Computer Vision and Pattern Recognition, July 2017.Google Scholar
  33. [33]
    Brigante C, Abbate N, Basile A, Faulisi A, Sessa S. Towards miniaturization of a mems-based wearable motion capture system. IEEE Transactions on Industrial Electronics, 2011, 58(8): 3234-3241.CrossRefGoogle Scholar
  34. [34]
    Andrews S, Huerta I, Komura T, Sigal L, Mitchell K. Real-time physics-based motion capture with sparse sensors. In Proc. the 13th European Conference on Visual Media Production, Dec. 2016.Google Scholar
  35. [35]
    Hou J, Chau L P, Magnenat-Thalmann N, He Y. Human motion capture data tailored transform coding. IEEE Trans actions on Visualization and Computer Graphics, 2015, 21(7): 848-859.CrossRefGoogle Scholar
  36. [36]
    Vlasic D, Baran I, Matusik W, Popović J. Articulated mesh animation from multiview silhouettes. ACM Transactions on Graphics, 2008, 27(3): Article No. 97.Google Scholar
  37. [37]
    Liu Y, Stoll C, Gall J, Seidel H P, Theobalt C. Marker-less motion capture of interacting characters using multi-view image segmentation. In Proc. the IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2011, pp.1249-1256.Google Scholar
  38. [38]
    Hasler N, Rosenhahn B, Thormahlen T, Wand M, Gall J, Seidel H P. Markerless motion capture with unsynchronized moving cameras. In Proc. the IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2009, pp.224-231.Google Scholar
  39. [39]
    Shiratori T, Park H S, Sigal L, Sheikh Y, Hodgins J K. Motion capture from bodymounted cameras. ACM Transactions on Graphics, 2011, 30(4): Article No. 31.Google Scholar
  40. [40]
    Elhayek A, Aguiar E, Jain A, Tompson J, Pishchulin L, Andriluka M, Bregler C, Schiele B, Theobalt C. Efficient convnetbased marker-less motion capture in general scenes with a low number of cameras. In Proc. the IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2015, pp.3810-3818.Google Scholar
  41. [41]
    Ionescu C, Papava D, Olaru V, Sminchisescu C. Human3.6M: Large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7): 1325-1339.CrossRefGoogle Scholar
  42. [42]
    Sigal L, Balan A O, Black M J. HumanEva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. International Journal of Computer Vision, 2010, 87: 4-27.Google Scholar
  43. [43]
    Dantone M, Gall J, Leistner C, van Gool L. Body parts dependent joint regressors for human pose estimation in still images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(11): 2131-2143.CrossRefGoogle Scholar
  44. [44]
    Wei S E, Ramakrishna V, Kanade T, Sheikh Y. Convolutional pose machines. In Proc. the IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2016, pp. 4724-4732.Google Scholar
  45. [45]
    Wei X, Chai J. Videomocap: Modeling physically realistic human motion from monocular video sequences. ACM Transactions on Graphics, 2010, 29(4): Article No. 42.Google Scholar
  46. [46]
    Insafutdinov E, Pishchulin L, Andres B, Andriluka M, Schiele B. DeeperCut: A deeper, stronger, and faster multi-person pose estimation model. In Proc. the European Conference on Computer Vision, Oct. 2016, pp.34-50.Google Scholar
  47. [47]
    Andriluka M, Pishchulin L, Gehler P, Schiele B. 2D human pose estimation: New benchmark and state of the art analysis. In Proc. the IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2014, pp.3686-3693.Google Scholar
  48. [48]
    Baak A, Müller M, Bharaj G, Seidel H P, Theobalt C. A data-driven approach for real-time full body pose reconstruction from a depth camera. In Consumer Depth Cameras for Computer Vision, Fossati A, Gall J, Grabrier H et al. (eds.), Springer, 2013, pp.71-98.Google Scholar
  49. [49]
    Ye M, Wang X, Yang R, Ren L, Pollefeys M. Accurate 3D pose estimation from a single depth image. In Proc. the International Conference on Computer Vision, Nov. 2011, pp. 731-738.Google Scholar
  50. [50]
    Liu Z, Zhou L, Leung H, Shum H P. Kinect posture reconstruction based on a local mixture of gaussian process models. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(11): 2437-2450.CrossRefGoogle Scholar
  51. [51]
    Wei X, Zhang P, Chai J. Accurate realtime full-body motion capture using a single depth camera. ACM Transactions on Graphics, 2012, 31(6): Article No. 188.Google Scholar
  52. [52]
    Zhang P, Siu K, Zhang J, Liu C K, Chai J. Leveraging depth cameras and wearable pressure sensors for fullbody kinematics and dynamics capture. ACM Transactions on Graphics, 2014, 33(6): Article No. 221.Google Scholar
  53. [53]
    von Marcard T, Ponsmoll G, Rosenhahn B. Human pose estimation from video and IMUs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(8): 1533-1547.CrossRefGoogle Scholar
  54. [54]
    Arikan O, Forsyth D. Interactive motion generation from examples. ACM Transactions on Graphics, 2002, 21(3): 483-490.CrossRefMATHGoogle Scholar
  55. [55]
    Kovar L, Gleicher M, Pighin F. Motion graphs. ACM Transactions on Graphics, 2002, 21(3): 473-482.CrossRefGoogle Scholar
  56. [56]
    Ikemoto L, Forsyth D A. Enriching a motion collection by transplanting limbs. In Proc. the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Aug. 2004, pp. 99-108.Google Scholar
  57. [57]
    Gleicher M. Motion path editing. In Proc. the Symposium on Interactive 3D Graphics, Mar. 2001, pp.195-202.Google Scholar
  58. [58]
    Huang Y, Kallmann M. Planning motions and placements for virtual demonstrators. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(5): 1568-1579.CrossRefGoogle Scholar
  59. [59]
    Kim Y, Park H, Bang S, Lee S H. Retargeting human-object interaction to virtual avatars. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(11): 2405-2412.CrossRefGoogle Scholar
  60. [60]
    Mukai T, Kuriyama S. Geostatistical motion interpolation. ACM Transactions on Graphics, 2005, 24(3): 1062-1070.CrossRefGoogle Scholar
  61. [61]
    Kovar L, Gleicher M. Automated extraction and parameterization of motions in large data sets. ACM Transactions on Graphics, 2004, 23(3): 559-568.CrossRefGoogle Scholar
  62. [62]
    Wang H, Ho E S, Komura T. An energydriven motion planning method for two distant postures. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(1): 18-30.CrossRefGoogle Scholar
  63. [63]
    Tanco L M, Hilton A. Realistic synthesis of novel human movements from a database of motion capture examples. In Proc. Workshop on Human Motion, Dec. 2000, pp.137-142.Google Scholar
  64. [64]
    Pullen K, Bregler C. Animating by multilevel sampling. In Proc. Computer Animation, May 2000, pp.36-42.Google Scholar
  65. [65]
    Hsu E, Pulli K, Popović J. Style translation for human motion. ACM Transactions on Graphics, 2005, 24(3): 1082-1089.CrossRefGoogle Scholar
  66. [66]
    Chai J, Hodgins J K. Constraint-based motion optimization using a statistical dynamic model. ACM Transactions on Graphics, 2007, 26(3): Article No. 8.Google Scholar
  67. [67]
    Lau M, Bar-Joseph Z, Kuffner J. Modeling spatial and temporal variation in motion data. ACM Transactions on Graphics, 2009, 28(5): Article No. 171.Google Scholar
  68. [68]
    Min J, Chai J. Motion graphs++: A compact generative model for semantic motion analysis and synthesis. ACM Transactions on Graphics, 2012, 31(6): Article No. 153.Google Scholar
  69. [69]
    Holden D, Saito J, Komura T, Joyce T. Learning motion manifolds with convolutional autoencoders. In Proc. SIG-GRAPH Asia 2015 Technical Briefs, Nov. 2015.Google Scholar
  70. [70]
    Holden D, Saito J, Komura T. A deep learning framework for character motion synthesis and editing. ACM Transactions on Graphics, 2016, 35(4): Article No. 138.Google Scholar
  71. [71]
    Ikemoto L, Arikan O, Forsyth D. Generalizing motion edits with Gaussian processes. ACM Transactions on Graphics, 2009, 28(1): Article No. 1.Google Scholar
  72. [72]
    Xia S, Wang C, Chai J, Hodgins J K. Realtime style transfer for unlabeled heterogeneous human motion. ACM Transactions on Graphics, 2015, 34(4): Article No. 119.Google Scholar
  73. [73]
    Brand M, Hertzmann A. Style machines. In Proc. the 27th Annual Conference on Computer Graphics and Interactive Techniques, Jul. 2000, pp.183-192.Google Scholar
  74. [74]
    Wang J M, Fleet D J, Hertzmann A. Multifactor Gaussian process models for style-content separation. In Proc. the 24th International Conference on Machine Learning, Jun. 2007, pp.975-982.Google Scholar
  75. [75]
    Min J, Liu H, Chai J. Synthesis and editing of personalized stylistic human motion. In Proc. the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, Feb. 2010, pp.39-46.Google Scholar
  76. [76]
    Ma W, Xia S, Hodgins J K, Yang X, Li C, Wang Z. Modeling style and variation in human motion. In Proc. the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Jul. 2010, pp.21-30.Google Scholar
  77. [77]
    Drumwright E. A fast and stable penalty method for rigid body simulation. IEEE Transactions on Visualization and Computer Graphics, 2008, 14(1): 231-240.CrossRefGoogle Scholar
  78. [78]
    Wieber P B. On the stability of walking systems. In Proc. the International Workshop on Humanoid and Human Friendly Robotics, Dec. 2002.Google Scholar
  79. [79]
    Yin K, Loken K, Panne M. SIMBICON: Simple biped locomotion control. ACM Transactions on Graphics, 2007, 26(3): Article No. 105.Google Scholar
  80. [80]
    Coros S, Beaudoin P, Panne M. Generalized biped walking control. ACM Transactions on Graphics, 2010, 29(4): Article No. 130.Google Scholar
  81. [81]
    Wang J M, Fleet D J, Hertzmann A. Optimizing walking controllers. ACM Transactions on Graphics, 2009, 28(5): Article No. 168.Google Scholar
  82. [82]
    Wang J, Hamner S R, Delp S L, Koltun V. Optimizing locomotion controllers using biologically-based actuators and objectives. ACM Transactions on Graphics, 2012, 31(4): Article No. 25.Google Scholar
  83. [83]
    Liu L, Yin K, de Panne M V, Guo B. Terrain runner: Control, parameterization, composition, and planning for highly dynamic motions. ACM Transactions on Graphics, 2012, 31(6): Article No. 154.Google Scholar
  84. [84]
    Liu L, de Panne M V, Yin K. Guided learning of control graphs for physics-based characters. ACM Transactions on Graphics, 2016, 35(3): Article No. 29.Google Scholar
  85. [85]
    Zordan V B, Hodgins J K. Motion capture-driven simulations that hit and react. In Proc. the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Jul. 2002, pp.89-96.Google Scholar
  86. [86]
    Zordan V B, Majkowska A, Chiu B, Fast M. Dynamic response for motion capture animation. ACM Transactions on Graphics, 2005, 24(3): 697-701.CrossRefGoogle Scholar
  87. [87]
    Sharon D, Panne M. Synthesis of controllers for stylized planar bipedal walking. In Proc. the IEEE International Conference on Robotics and Automation, Apr. 2005, pp. 2387-2392.Google Scholar
  88. [88]
    Sok K W, Kim M, Lee J. Simulating biped behaviors from human motion data. ACM Transactions on Graphics, 2007, 26(3): Article No. 107.Google Scholar
  89. [89]
    Silva M, Abe Y, Popović J. Interactive simulation of stylized human locomotion. ACM Transactions on Graphics, 2008, 27(3): Article No. 82.Google Scholar
  90. [90]
    Geijtenbeek T, Pronost N, Stappen A F. Simple data-driven control for simulated bipeds. In Proc. the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Jul. 2012, pp.211-219.Google Scholar
  91. [91]
    Hamalainen P, Rajamaki J, Liu C K. Online control of simulated humanoids using particle belief propagation. ACM Transactions on Graphics, 2015, 34(4): Article No. 81.Google Scholar
  92. [92]
    Agrawal S, de Panne M V. Taskbased locomotion. ACM Transactions on Graphics, 2016, 35(4): Article No. 82.Google Scholar
  93. [93]
    Yeadon M R. The simulation of aerial movement-II. A mathematical inertia model of the human body. Journal of Biomechanics, 1990, 23(1): 67-74.CrossRefGoogle Scholar
  94. [94]
    Sheets A, Abrams G D, Corazza S, Safran M R, Andriacchi T P. Kinematics differences between the flat, kick, and slice serves measured using a markerless motion capture method. Annals of Biomedical Engineering, 2011, 39(12): 3011-3020.CrossRefGoogle Scholar
  95. [95]
    Lv X, Chai J, Xia S. Data-driven inverse dynamics for human motion. ACM Transactions on Graphics, 2016, 35(6): Article No. 163.Google Scholar
  96. [96]
    Witkin A, Kass M. Spacetime constraints. ACM SIGGRAPH Computer Graphics, 1988, 22(4): 159-168.CrossRefGoogle Scholar
  97. [97]
    Liu C K, Hertzmann A, Popović Z. Composition of complex optimal multicharacter motions. In Proc. the 2006 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Sept. 2006, pp.215-222.Google Scholar
  98. [98]
    Borno M A, de Lasa M, Hertzmann A. Trajectory optimization for full-body movements with complex contacts. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(8): 1405-1414.CrossRefGoogle Scholar
  99. [99]
    Park C, Park J S, Tonneau S, Mansard N, Multon F, Pettre J, Manocha D. Dynamically balanced and plausible trajectory planning for human-like characters. In Proc. the 20th ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, Feb., 2016, pp.39-48.Google Scholar
  100. [100]
    Geijtenbeek T, Pronost N. Interactive character animation using simulated physics: A state-of-the-art review. Computer Graphics Forum, 2012, 31(8): 2492-2515.CrossRefGoogle Scholar
  101. [101]
    Abe Y, da Silva M, Popović J. Multiobjective control with frictional contacts. In Proc. the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Aug. 2007, pp.249-258.Google Scholar
  102. [102]
    Wu C C, Zordan V. Goal-directed stepping with momentum control. In Proc. the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Jul. 2010, pp.113-118.Google Scholar
  103. [103]
    de Lasa M, Mordatch I, Hertzmann A. Feature-based locomotion controllers. ACM Transactions on Graphics, 2010, 29(4): Article No. 131.Google Scholar
  104. [104]
    Muico U, Lee Y, Popović J, Popović Z. Contact-aware nonlinear control of dynamic characters. ACM Transactions on Graphics, 2009, 28(3): Article No. 81.Google Scholar
  105. [105]
    Muico U, Popović J, Popović Z. Composite control of physically simulated characters. ACM Transactions on Graphics, 2011, 30(3): Article No. 16.Google Scholar
  106. [106]
    Wu J C, Popović Z. Terrain-adaptive bipedal locomotion control. ACM Transactions on Graphics, 2010, 29(4): Article No. 72.Google Scholar
  107. [107]
    Kwon T, Hodgins J. Control systems for human running using an inverted pendulum model and a reference motion capture sequence. In Proc. the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Jul. 2010, pp.129-138.Google Scholar
  108. [108]
    Mordatch I, de Lasa M, Hertzmann A. Robust physics-based locomotion using low-dimensional planning. ACM Transactions on Graphics, 2010, 29(4): Article No. 71.Google Scholar
  109. [109]
    Han D, Noh J, Jin X, Shin J S, Shin S Y. On-line real-time physics-based predictive motion control with balance recovery. Computer Graphics Forum, 2014, 33(2): 245-254.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Shihong Xia
    • 1
  • Lin Gao
    • 1
  • Yu-Kun Lai
    • 2
  • Ming-Ze Yuan
    • 1
    • 3
  • Jinxiang Chai
    • 4
  1. 1.Advanced Computing Research Laboratory, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.School of Computer Science and InformaticsCardiff UniversityCardiffU.K.
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Department of Computer Science and EngineeringTexas A&M UniversityCollege StationU.S.A.

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