Learning and Transferring Motion Style using Sparse PCA

Abstract

Motion style transfer is a primary problem in computer animation, allowing us to convert the motion of an actor to that of another one. Myriads approaches have been developed to perform this task, however, the majority of them are data-driven, which require a large dataset and a time-consuming period for training a model in order to achieve good results. In contrast, we propose a novel method applied successfully for this task in a small dataset. This exploits Sparse PCA to decompose original motions into smaller components which are learned with particular constraints. The synthesized results are highly precise and smooth motions with its emotion as shown in our experiments.
Keywords

Sparse PCA, style learning, motion style transfer

References

[1] E. Hsu, K. Pulli, J. Popovi´cc, Style translation for human motion, ACM Transactions on
Graphics (TOG). 24 (3) (2005) 1082–1089. https://doi.org/10.1145/1073204.1073315.
[2] S. Xia, C. Wang, J. Chai, J. Hodgins, Realtime style transfer for unlabeled heterogeneous human motion,
ACM Transactions on Graphics (TOG). 34 (4) (2015) 119. https://doi.org/10.1145/2766999.
[3] L. A. Gatys, A. S. Ecker, M. Bethge, A neural algorithm of artistic style, arXiv preprint arXiv:1508.06576.
[4] D. Holden, J. Saito, T. Komura, A deep learning framework for character motion synthesis and editing,
ACM Transactions on Graphics (TOG). 35 (4) (2016) 138. https://doi.org/10.1145/2897824.2925975.
[5] H. Zou, T. Hastie, R. Tibshirani, Sparse principal component analysis, Journal of computational
and graphical statistics. 15 (2) (2006) 265–286. https://doi.org/10.1198/106186006X113430.
[6] I. T. Jollie, N. T. Trendafilov, M. Uddin, A modified principal component technique based
on the lasso, Journal of computational and Graphical Statistics 12 (3) (2003) 531–547. https://doi.org/10.1198/1061860032148.
[7] T. Neumann, K. Varanasi, S. Wenger, M. Wacker, M. Magnor, C. Theobalt, Sparse localized deformation
components, ACM Transactions on Graphics (TOG). 32 (6) (2013) 179.
https://doi.org/10.1145/2508363.2508417.
[8] T. F. Cox, M. A. Cox, Multidimensional scaling, second ed., CRC press, 2000.
[9] J. Shawe-Taylor, C. K. Williams, N. Cristianini, J. Kandola, On the eigenspectrum of the gram matrix and the generalization error of kernel-pca, IEEE Transactions on Information Theory. 51 (7) (2005).