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Chao Wang, Yang Liu, Xiaohu Guo, Zichun Zhong, Binh Le, Zhigang Deng. Spectral Animation Compression[J]. Journal of Computer Science and Technology, 2015, 30(3): 540-552. DOI: 10.1007/s11390-015-1544-z
Citation: Chao Wang, Yang Liu, Xiaohu Guo, Zichun Zhong, Binh Le, Zhigang Deng. Spectral Animation Compression[J]. Journal of Computer Science and Technology, 2015, 30(3): 540-552. DOI: 10.1007/s11390-015-1544-z

Spectral Animation Compression

Funds: Chao Wang, Xiaohu Guo, and Zichun Zhong are partially supported by the National Science Foundation under Grant Nos. IIS-1149737 and CNS-1012975.
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  • Author Bio:

    Chao Wang is a currently a Ph.D. candidate in the Department of Computer Science at University of Texas at Dallas. Before that, he received his M.S. degree in computer science in 2012, and B.S. degree in automation in 2009, both from Tsinghua University.

  • Corresponding author:

    Xiaohu Guo received his Ph.D. degree in computer science from Stony Brook University in 2006. He is currently an associate professor of computer science at University of Texas at Dallas. E-mail: xguo@utdallas.edu

  • Received Date: December 02, 2014
  • Revised Date: March 11, 2015
  • Published Date: May 04, 2015
  • This paper presents a spectral approach to compress dynamic animation consisting of a sequence of homeo-morphic manifold meshes. Our new approach directly compresses the field of deformation gradient defined on the surface mesh, by decomposing it into rigid-body motion (rotation) and non-rigid-body deformation (stretching/shearing) through polar decomposition. It is known that the Rotation Group has the algebraic topology of 3-D ring, which is different from other operations like stretching and shearing. Thus we compress these two groups separately, by using Manifold Harmonics Transform to drop out their high-frequency details. Our experimental result shows that our method achieves a good balance between the reconstruction quality and compression ratio. We compare our results quantitatively with other existing approaches on animation compression, using standard measurement criteria.
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