Journal of Zhejiang University SCIENCE C

, Volume 13, Issue 3, pp 196–207 | Cite as

Synthesizing style-preserving cartoons via non-negative style factorization

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Abstract

We present a complete framework for synthesizing style-preserving 2D cartoons by learning from traditional Chinese cartoons. In contrast to reusing-based approaches which rely on rearranging or retrieving existing cartoon sequences, we aim to generate stylized cartoons with the idea of style factorization. Specifically, starting with 2D skeleton features of cartoon characters extracted by an improved rotoscoping system, we present a non-negative style factorization (NNSF) algorithm to obtain style basis and weights and simultaneously preserve class separability. Thus, factorized style basis can be combined with heterogeneous weights to re-synthesize style-preserving features, and then these features are used as the driving source in the character reshaping process via our proposed subkey-driving strategy. Extensive experiments and examples demonstrate the effectiveness of the proposed framework.

Key words

Character cartoon Machine learning Cartoon synthesis 

CLC number

TP391 

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References

  1. Agarwala, A., Hertzmann, A., Salesin, D.H., Seitz, S.M., 2004. Keyframe-based tracking for rotoscoping and animation. ACM Trans. Graph., 23(3):584–591. [doi:10.1145/1015706.1015764]CrossRefGoogle Scholar
  2. Aharon, M., Elad, M., Bruckstein, A., 2006. K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process., 54(11):4311–4322. [doi:10.1109/TSP.2006.881199]CrossRefGoogle Scholar
  3. Alexa, M., Cohen-Or, D., Levin, D., 2000. As-Rigid-as-Possible Shape Interpolation. Proc. SIGGRAPH, p.157–164. [doi:10.1145/344779.344859]Google Scholar
  4. Brand, M., Hertzmann, A., 2000. Style Machines. Proc. SIGGRAPH, p.183–192. [doi:10.1145/344779.344865]Google Scholar
  5. Bregler, C., Loeb, L., Chuang, E., Deshpande, H., 2002. Turning to the masters: motion capturing cartoons. ACM Trans. Graph., 21(3):399–407. [doi:10.1145/566654.566595]CrossRefGoogle Scholar
  6. Chenney, S., Pingel, M., Iverson, R., Szymanski, M., 2002. Simulating Cartoon Style Animation. Proc. NPAR, p.133–138. [doi:10.1145/508530.508553]Google Scholar
  7. Freifeld, O., Weiss, A., Zuffi, S., Black, M.J., 2010. Contour People: a Parameterized Model of 2D Articulated Human Shape. Proc. CVPR, p.639–646. [doi:10.1109/CVPR.2010.5540154]Google Scholar
  8. Guan, P., Freifeld, O., Black, M., 2010. A 2D Human Body Model Dressed in Eigen Clothing. Proc. ECCV, p.285–298.Google Scholar
  9. Hoch, M., Litwinowicz, P.C., 1996. A semi-automatic system for edge tracking with snakes. Vis. Comput., 12(2):75–83. [doi:10.1007/s003710050049]Google Scholar
  10. Hornung, A., Dekkers, E., Kobbelt, L., 2007. Character animation from 2D pictures and 3D motion data. ACM Trans. Graph., 26(1):1–es. [doi:10.1145/1189762.1189763]CrossRefGoogle Scholar
  11. Hoyer, P.O., 2002. Non-negative Sparse Coding. Proc. 12th IEEE Workshop on Neural Networks for Signal Processing, p.557–565. [doi:10.1109/NNSP.2002.1030067]Google Scholar
  12. Hsu, E., Pulli, K., Popović, J., 2005. Style translation for human motion. ACM Trans. Graph., 24(3):1082–1089. [doi:10.1145/1073204.1073315]CrossRefGoogle Scholar
  13. Igarashi, T., Moscovich, T., Hughes, J.F., 2005. As-rigid-as-possible shape manipulation. ACM Trans. Graph., 24(3): 1134–1141. [doi:10.1145/1073204.1073323]CrossRefGoogle Scholar
  14. Jonker, R., Volgenant, A., 1987. A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing, 38(4):325–340. [doi:10.1007/BF02278710]MathSciNetMATHCrossRefGoogle Scholar
  15. Kuo, P., Makris, D., Megherbi, N., Nebel, J.C., 2008. Integration of local image cues for probabilistic 2D pose recovery. LNCS, 5359:214–223. [doi:10.1007/978-3-540-89646-3_21]Google Scholar
  16. Kwon, J., Lee, I.K., 2008. Exaggerating character motions using sub-joint hierarchy. Comput. Graph. Forum, 27(6): 1677–1686. [doi:10.1111/j.1467-8659.2008.01177.x]MathSciNetMATHCrossRefGoogle Scholar
  17. Lau, M., Chai, J., Xu, Y.Q., Shum, H.Y., 2009. Face poser: interactive modeling of 3D facial expressions using facial priors. ACM Trans. Graph., 29(1):1–17. [doi:10.1145/1640443.1640446]CrossRefGoogle Scholar
  18. Lee, D.D., Seung, H.S., 2001. Algorithms for Non-negative Matrix Factorization. Proc. NIPS, 13:556–562.Google Scholar
  19. Li, Y., Gleicher, M., Xu, Y.Q., Shum, H.Y., 2003. Stylizing Motion with Drawings. Proc. SCA, p.309–319.Google Scholar
  20. Ma, X., Le, B.H., Deng, Z., 2009. Style Learning and Transferring for Facial Animation Editing. Proc. SCA, p.123–132. [doi:10.1145/1599470.1599486]Google Scholar
  21. Moeslund, T.B., Hilton, A., Krüger, V., 2006. A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Understand., 104(2–3):90–126. [doi:10.1016/j.cviu.2006.08.002]CrossRefGoogle Scholar
  22. Pullen, K., Bregler, C., 2002. Motion capture assisted animation: texturing and synthesis. ACM Trans. Graph., 21(3): 501–508. [doi:10.1145/566654.566608]CrossRefGoogle Scholar
  23. Rogez, G., Orrite-Uruñuela, C., Martínez-del-Rincón, J., 2008. A spatio-temporal 2D-models framework for human pose recovery in monocular sequences. Pattern Recogn., 41(9):2926–2944. [doi:10.1016/j.patcog.2008.02.012]MATHCrossRefGoogle Scholar
  24. Schaefer, S., McPhail, T., Warren, J., 2006. Image deformation using moving least squares. ACM Trans. Graph., 25(3): 533–540. [doi:10.1145/1141911.1141920]CrossRefGoogle Scholar
  25. Sýkora, D., Sedlacek, D., Jinchao, S., Dingliana, J., Collins, S., 2010. Adding depth to cartoons using sparse depth (in)equalities. Comput. Graph. Forum, 29(2):615–623. [doi:10.1111/j.1467-8659.2009.01631.x]CrossRefGoogle Scholar
  26. Tenenbaum, J.B., Freeman, W.T., 2000. Separating style and content with bilinear models. Neur. Comput., 12(6): 1247–1283. [doi:10.1162/089976600300015349]CrossRefGoogle Scholar
  27. Tenenbaum, J.B., Silva, V., Langford, J.C., 2000. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319–2323. [doi:10.1126/science.290.5500.2319]CrossRefGoogle Scholar
  28. Torresani, L., Hackney, P., Bregler, C., 2007. Learning Motion Style Synthesis from Perceptual Observations. Proc. NIPS, 19:1393–1400.Google Scholar
  29. Wang, H., Li, H., 2002. Cartoon Motion Capture by Shape Matching. Proc. Conf. on Computer Graphics and Applications, p.454–456.Google Scholar
  30. Wang, J., Drucker, S.M., Agrawala, M., Cohen, M.F., 2006. The cartoon animation filter. ACM Trans. Graph., 25(3): 1169–1173. [doi:10.1145/1141911.1142010]CrossRefGoogle Scholar
  31. Wang, J.M., Fleet, D.J., Hertzmann, A., 2007. Multifactor Gaussian Process Models for Style-Content Separation. Proc. ICML, p.975–982. [doi:10.1145/1273496.1273619]Google Scholar
  32. Weng, Y., Xu, W., Wu, Y., Zhou, K., Guo, B., 2006. 2D shape deformation using nonlinear least squares optimization. Vis. Comput., 22(9–11):653–660. [doi:10.1007/s00371-006-0054-y]CrossRefGoogle Scholar
  33. Yan, H.B., Hu, S., Martin, R.R., Yang, Y.L., 2008. Shape deformation using a skeleton to drive simplex transformations. IEEE Trans. Visual. Comput. Graph., 14(3):693–706. [doi:10.1109/TVCG.2008.28]CrossRefGoogle Scholar
  34. Yang, Y., Zhuang, Y., Xu, D., Pan, Y., Tao, D., Maybank, S., 2009. Retrieval Based Interactive Cartoon Synthesis via Unsupervised Bi-distance Metric Learning. Proc. Conf. on Multimedia, p.311–320. [doi:10.1145/1631272.1631316]Google Scholar
  35. Zhou, S., Fu, H., Liu, L., Cohen-Or, D., Han, X., 2010. Parametric Reshaping of Human Bodies in Images. Proc. SIGGRAPH, p.1–10. [doi:10.1145/1778765.1778863]Google Scholar

Copyright information

© Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of Artificial Intelligence, School of Computer Science and TechnologyZhejiang UniversityHangzhouChina

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