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Stylized human motion warping method based on identity-independent coordinates

  • Lei LyuEmail author
  • Jinling Zhang
Methodologies and Application
  • 16 Downloads

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.

Keywords

Human motion simulation Motion capture Motion warping Identity-independent coordinates 

Notes

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).

Compliance with ethical standards

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.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringShandong Normal UniversityJinanChina
  2. 2.Shandong Provincial Key Laboratory for Distributed Computer Software Novel TechnologyJinanChina
  3. 3.School of InformationRenmin University of ChinaBeijingChina

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