›› 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.

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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.

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