Skip to main content
SpringerLink
Log in

Stylized human motion warping method based on identity-independent coordinates

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Human motion style is a vital concept to virtual human that has a great impact on the expressiveness of the final animation. This paper presents a novel technique that transfers style between heterogeneous motions in real time. Unlike previous approaches, our stylized motion warping is capable of reusing style between heterogeneous motions. The key idea of our work is to represent human motions with identity-independent coordinates (IICs) and learn relative space–time transformations between stylistically different IICs, instead of separating style from content. Once the relative space–time transformations are estimated from a small set of stylized example motions whose contents are identical, our technique is capable of generating style-controllable human motions by applying these transformations to heterogeneous motions with simple linear operations. Experimental results demonstrate that our technique is efficient and powerful in stylized human motion generation. Besides, our technique can also be used in numerous interactive applications, such as real-time human motion style control, stylizing motion graphs and style-based human motion editing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Amaya K, Bruderlin A, Calvert T (1996) Emotion from motion. In: Proceedings of the conference on Graphics interface, vol 96, pp 222–229

  • Arikan O, Forsyth DA (2002) Interactive motion generation from examples. ACM Trans Graph 21(3):483–490

    Article  MATH  Google Scholar 

  • Aristidou A, Stavrakis E, Papaefthimiou M, Papagiannakis G, Chrysanthou Y (2017) Style-based motion analysis for dance composition. Vis Comput 3:1–13

    Google Scholar 

  • Beaudoin P, Coros S, Panne MVD, Poulin P (2008) Motion-motif graphs. In: Proceedings 2008 ACM SIGGRAPH/Eurographics symposium on computer animation, pp 117–126

  • Brand M (2000) Style machines. In: Siggraph computer graphics proceedings, pp 183–192

  • Bruderlin A, Calvert TW (1989) Goal-directed, dynamic animation of human walking. In: Conference on computer graphics and interactive techniques, pp 233–242

    Article  Google Scholar 

  • Cilardo A (2018) Htcomp: bringing reconfigurable hardware to future high-performance applications. Int J High Perform Comput Netw 12:74. https://doi.org/10.1504/IJHPCN.2018.10015028

    Article  Google Scholar 

  • Foroud A, Whishaw IQ (2006) Changes in the kinematic structure and non-kinematic features of movements during skilled reaching after stroke: a Laban movement analysis in two case studies. J Neurosci Methods 158(1):137–149

    Article  Google Scholar 

  • Grochow K, Martin SL, Hertzmann A (2004) Style-based inverse kinematics. ACM Trans Graph 23(3):522–531

    Article  Google Scholar 

  • Gupta BB (2017) Computer and cyber security: principles, algorithm, applications and perspectives. CRC Press, Boca Raton

    Google Scholar 

  • Gupta Brij DPA, Yamaguchi S (2016) Handbook of research on modern cryptographic solutions for computer and cyber security. IGI Global, Pennsylvania

    Book  Google Scholar 

  • Hauser K, Bretl T, Harada K, Latombe JC (2006) Using motion primitives in probabilistic sample-based planning for humanoid robots. In: The workshop on algorithmic foundations of robotics, pp 507–522

    MATH  Google Scholar 

  • Heck R, Gleicher M (2007) Parametric motion graphs. In: Symposium on interactive 3d graphics and games, pp 129–136

  • Hong L, Liu B, Hao Z, Liang L, Xin Q, Zhang G (2018a) Crowd evacuation simulation approach based on navigation knowledge and two-layer control mechanism. Inf Sci 436–437:247–267

    MathSciNet  Google Scholar 

  • Hong L, Xu B, Lu D, Zhang G (2018b) A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm. Appl Soft Comput 68:360–376

    Article  Google Scholar 

  • Hsu E, Pulli K (2005) Style translation for human motion. ACM Trans Graph 24(3):1082–1089

    Article  Google Scholar 

  • Ikemoto L, Forsyth DA (2004) Enriching a motion collection by transplanting limbs. In: Symposium on computer animation, pp 99–108

  • Jang WS, Lee WK, Lee IK, Lee J (2008) Enriching a motion database by analogous combination of partial human motions. Vis Comput Int J Comput Graph 24(4):271–280

    Google Scholar 

  • Kang HL, Choi MG, Lee J (2006) Motion patches: building blocks for virtual environments annotated with motion data. In: ACM SIGGRAPH, pp 898–906

  • Korein JU, Badler NI (1982) Techniques for generating the goal-directed motion of articulated structures. IEEE Comput Graph Appl 2(9):71–81

    Article  Google Scholar 

  • Kovar L, Gleicher M (2002) Motion graphs. In: Siggraph 02: conference on computer graphics and interactive techniques, pp 473–482

  • Kovar L, Gleicher M (2003) Flexible automatic motion blending with registration curves. In: ACM Siggraph/Eurographics symposium on computer animation, pp 214–224

  • Kovar L, Gleicher M (2004) Automated extraction and parameterization of motions in large data sets. ACM Trans Graph 23(3):559–568

    Article  Google Scholar 

  • Lee J, Shin SY (1999) A hierarchical approach to interactive motion editing for human-like figures. Siggraph 99:39–48

    Google Scholar 

  • Levine S, Wang JM, Haraux A, Koltun V (2012) Continuous character control with low-dimensional embeddings. ACM Trans Graph 31(4):1–10

    Article  Google Scholar 

  • Li Y, Wang T, Shum HY (2002) Motion texture: a two-level statistical model for character motion synthesis. In: Conference on computer graphics and interactive techniques, pp 465–472

  • Mukai T, Kuriyama S (2005) Geostatistical motion interpolation. ACM Trans Graph 24(3):1062–1070

    Article  Google Scholar 

  • Neff M, Kim Y (2009) Interactive editing of motion style using drives and correlations. In: ACM Siggraph/Eurographics symposium on computer animation, SCA 2009. Louisiana, pp 103–112

  • Pan W, Torresani L (2009) Unsupervised hierarchical modeling of locomotion styles. In: International conference on machine learning, pp 785–792

  • Panda SK, Naik S (2018) An efficient data replication algorithm for distributed systems. Int J Cloud Appl Comput 8(3):60–77

    Google Scholar 

  • Paul RP (1981) Robot manipulator: mathematics, programming and control: the computer control of robot manipulators. MIT Press, Cambridge

    Google Scholar 

  • Rose C, Cohen MF, Bodenheimer B (2002) Verbs and adverbs: multidimensional motion interpolation. IEEE Comput Graph Appl 18(5):32–40

    Article  Google Scholar 

  • Sang IP, Shin HJ, Shin SY (2002) On-line locomotion generation based on motion blending. In: ACM Siggraph/Eurographics symposium on computer animation, pp 105–111

  • Shapiro A, Cao Y, Faloutsos P (2006) Style components. In: Graphics interface, pp 33–39

  • Shin HJ (2001) Computer puppetry: an importance-based approach. ACM Trans Graph TOG 20(2):67–94

    Article  Google Scholar 

  • Shin HJ, Oh HS (2006) Fat graphs: constructing an interactive character with continuous controls. In: ACM Siggraph/Eurographics symposium on computer animation, SCA 2006. Austria, pp 291–298

  • Sung Y, Guo H, Lee SG (2018) Motion quaternion-based motion estimation method of myo using k-means algorithm and bayesian probability. Soft Comput 4:1–11

    Google Scholar 

  • Tolani D, Goswami A, Badler NI (2000) Real-time inverse kinematics techniques for anthropomorphic limbs. Graph Models 62(5):353–388

    Article  MATH  Google Scholar 

  • Torre FEDL, Black MJ (2001) Dynamic coupled component analysis. Proc Comput Vision Pattern Recognit 2:643–651

    Google Scholar 

  • Treuille A, Lee Y (2007) Near-optimal character animation with continuous control. ACM Trans Graph 26(3):1–7

    Article  Google Scholar 

  • Unuma M, Anjyo K, Takeuchi R (1995) Fourier principles for emotion-based human figure animation. In: Conference on computer graphics and interactive techniques, pp 91–96

  • Urtasun R, Glardon P, Boulic R, Thalmann D, Fua P (2010) Style-based motion synthesis. Comput Graph Forum 23(4):799–812

    Article  Google Scholar 

  • Vasilescu MAO (2002) Human motion signatures: analysis, synthesis, recognition. In: International conference on pattern recognition, 2002 proceedings, vol 3, pp 456–460

  • Wang JM, Fleet DJ, Hertzmann A (2007) Multifactor Gaussian process models for style-content separation. In: International conference on machine learning, pp 975–982

  • Witkin A, Popovic Z (1995) Motion warping. In: Conference on computer graphics and interactive techniques, pp 105–108

  • Wu X, Ma L, Zheng C, Chen Y, Huang KS (2006) On-line motion style transfer. In: International conference on entertainment computing, pp 268–279

    Google Scholar 

  • Xiao Q, Liu S (2015) Motion retrieval based on dynamic Bayesian network and Canonical time warping. Soft Comput 2:1–14

    Article  Google Scholar 

  • Xiao Q, Song R (2016) Motion retrieval based on motion semantic dictionary and HMM inference. Soft Comput 21(1):1–11

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61976127), the Shandong Key Research and Development Program (No. 2017GSF20105), Major Program of Shandong Province Natural Science Foundation (No. ZR2018ZB0419), Natural Science Foundation of Shandong Province (No. ZR2016FB13), and Shandong Province Higher Educational Science and Technology Program (No. J16LN09).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Lei Lyu

  2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan, China

    Lei Lyu

  3. School of Information, Renmin University of China, Beijing, China

    Jinling Zhang

Authors
  1. Lei Lyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinling Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Lyu.

Ethics declarations

Conflict of interest

The author declares that there is no conflict of interest.

Ethical approval

The work of this article does not involve use of human participants or animals.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lyu, L., Zhang, J. Stylized human motion warping method based on identity-independent coordinates. Soft Comput 24, 9765–9775 (2020). https://doi.org/10.1007/s00500-019-04489-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04489-z

Keywords

Search

Navigation