›› 2013, Vol. 28 ›› Issue (3): 479-489.doi: 10.1007/s11390-013-1349-x

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Graphics, Visualization, and Image Processing • Previous Articles     Next Articles

Manifold Constrained Transfer of Facial Geometric Knowledge for 3D Caricature Reconstruction

Jun-Fa Liu1,2 (刘军发), Senior Member, CCF, Member, ACM, IEEE, Wen-Jing He1,2,3 (何文静), Tao Chen4 (陈涛), and Yi-Qiang Chen1,2 (陈益强), Senior Member, CCF, Member, ACM, IEEE   

  1. 1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
    2. Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing 100190, China;
    3. University of Chinese Academy of Sciences, Beijing 100049, China;
    4. School of Electronic Information and Automation, Chongqing University of Technology, Chongqing 400054, China
  • Received:2012-02-07 Revised:2013-01-23 Online:2013-05-05 Published:2013-05-05
  • Contact: 10.1007/s11390-013-1349-x
  • Supported by:

    This work was supported by the National Natural Foundation of China under Grant Nos. 61070110 and 61173066.

3D caricatures are important attractive elements of the interface in virtual environment such as online game. However, very limited 3D caricatures exist in the real world. Meanwhile, creating 3D caricatures manually is rather costly, and even professional skills are needed. This paper proposes a novel and effective manifold transfer algorithm to reconstruct 3D caricatures according to their original 2D caricatures. We first manually create a small dataset with only 100 3D caricature models and use them to initialize the whole 3D dataset. After that, manifold transfer algorithm is carried out to refine the dataset. The algorithm comprises of two steps. The first is to perform manifold alignment between 2D and 3D caricatures to get a "standard" manifold map; the second is to reconstruct all the 3D caricatures based on the manifold map. The proposed approach utilizes and transfers knowledge of 2D caricatures to the target 3D caricatures well. Comparative experiments show that the approach reconstructs 3D caricatures more effectively and the results conform more to the styles of the original 2D caricatures than the Principal Components Analysis (PCA) based method.

[1] Sadimon S B, Sunar M S, Mohamad D, Haron H. Computer generated caricature: A survey. In Proc. the 2010 Int. Conf. Cyberworlds Pages, Oct. 2010, pp.383-390.

[2] Kondo T, Murakami K, Koshimizu H. From coarse to fine correspondence of 3-D facial images and its application to 3-D facial caricaturing. In Proc. Int. Conf. Recent Advances in 3-D Digital Imaging and Modeling, May 1997, pp.283-288.

[3] Fujiwara T, Koshimizu H, Fujimura K et al. A method for 3D face modeling and caricatured figure generation. In Proc. ICME 2002, Aug. 2002, pp.137-140.

[4] Shadbolt A. From 2D photographs to 3D caricatures. Technical Report, Department of Computer Science, University of Sheffield, 2003.

[5] Lim Y K, Fedorov A, Kim S D. 3D caricature generation system on the mobile handset using a single photograph. In Proc. ICPPW2007, Sept. 2007, Article No.37.

[6] Clarke L, Chen M, Mora B. Automatic generation of 3D caricatures based on artistic deformation styles. IEEE Trans. Visualization and Computer Graphics, 2011, 17(6): 808-821.

[7] Liu J F, Chen Y Q, Miao C Y et al. Semi-supervised learning in reconstructed manifold space for 3D caricature generation. Computer Graphics Forum, 2009, 28(8): 2104-2116.

[8] Li P F, Chen Y Q, Liu J F et al. 3D caricature generation by nonlinear manifold learning. In Proc. ICME, June 2008, pp.941-944.

[9] Fu G H, Chen Y Q, Liu J F et al. Interactive expressive 3D caricatures design. In Proc. ICME, June 2008, pp.965-968.

[10] Akleman E, Reisch J. Modeling expressive 3D caricatures. In Proc. the 31st SIGGRAPH, Aug. 2004, p.61.

[11] Akimoto T, Suenaga Y, Wallace R S. Automatic creation of 3D facial models. Computer Graphics and Applications, 1993, 13(5): 16-22.

[12] Pighin F, Hecker J, Lischinski D, Szeliski R, Shalesin D H. Synthesizing realistic facial expressions from photographs. In Proc. the 25th SIGGRAPH, July 1998, pp.75-84.

[13] Liu Z, Zhang Z, Jacobs C, Cohen M. Rapid modeling of animated faces from video. Technical Report, MSR-TR-2000-11, Microsoft Research, February 2000.

[14] Blanz V, Vetter T. A morphable model for the synthesis of 3D-faces. In Proc. the 26th SIGGRAPH, Aug. 1999, pp.187194.

[15] Jiang D, Hu Y, Yan S et al. Efficient 3D reconstruction for face recognition. Pattern Recognition, 2005, 38(6): 787-798.

[16] Lee J, Moghaddam B, Pfister H, Machiraju R. Silhouettebased 3D face shape recovery. In Proc. Graphics Interface 2003, June 2003, pp.21-30.

[17] Nandy D, Ben-Arie J. Shape from recognition: A novel approach for 3-D face shape recovery. IEEE Transactions on Image Processing, 2001, 10(2): 206-217.

[18] Andreetto M, Brusco N, Cortelazzo G M. Automatic 3D modeling of textured cultural heritage objects. IEEE Transactions on Image Processing, 2004, 13(3): 354-369.

[19] Roy-Chowdhury A K, Chellappa R. Statistical bias in 3-D reconstruction from a monocular video. IEEE Transactions on Image Processing, 2005, 14(8): 1057-1062.

[20] Ham J, Lee D, Saul L. Semisupervised alignment of manifolds. In Proc. the 10th International Workshop on Artificial Intelligence and Statistics, Jan. 2005, pp.187-194.

[21] Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(5500): 23232326.

[22] Oliver M A, Webster R. Kriging: A method of interpolation for geographical information system. INT. J. Geographical Information Systems, 1990, 4(3): 313-332.

[23] Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 1977, 39(1): 1-38.

[24] Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 2003, 15(6): 1373-1396.
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[1] Feng Yulin;. Hierarchical Protocol Analysis by Temporal Logic[J]. , 1988, 3(1): 56 -69 .
[2] Zhang Bo; Zhang Ling;. A Relation Matrix Approach to Labelling Temporal Relations in Scheduling[J]. , 1991, 6(4): 339 -346 .
[3] Jian-Hua Feng, Yu-Guo Liao, and Yong Zhang. HCH for checking containment of XPath fragment[J]. , 2007, 22(5): 736 -748 .
[4] Zhang-Lin Cheng, Xiao-Peng Zhang, and Bao-Quan Chen. Simple Reconstruction of Tree Branches from a Single Range Image[J]. , 2007, 22(6): 846 -858 .
[5] Feng Xu, Member, CCF, Jing Pan, and Wen Lu. A Trust-Based Approach to Estimating the Confidence of the Software System in Open Environments[J]. , 2009, 24(2): 373 -385 .
[6] Jing Zhou, Shan-Feng Zhu, Xiaodi Huang, Yanchun Zhang. Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning[J]. , 2015, 30(4): 859 -873 .
[7] Tao Xie. Preface[J]. , 2016, 31(5): 849 -850 .
[8] Dongchul Park, Weiping He, David H. C. Du. Hot Data Identification with Multiple Bloom Filters: Block-Level Decision vs I/O Request-Level Decision[J]. , 2018, 33(1): 79 -97 .

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