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Dense Semantic and Topological Correspondence of 3D Faces without Landmarks

  • Zhenfeng Fan
  • Xiyuan Hu
  • Chen Chen
  • Silong Peng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11220)

Abstract

Many previous literatures use landmarks to guide the correspondence of 3D faces. However, these landmarks, either manually or automatically annotated, are hard to define consistently across different faces in many circumstances. We propose a general framework for dense correspondence of 3D faces without landmarks in this paper. The dense correspondence goal is revisited in two perspectives: semantic and topological correspondence. Starting from a template facial mesh, we sequentially perform global alignment, primary correspondence by template warping, and contextual mesh refinement, to reach the final correspondence result. The semantic correspondence is achieved by a local iterative closest point (ICP) algorithm of kernelized version, allowing accurate matching of local features. Then, robust deformation from the template to the target face is formulated as a minimization problem. Furthermore, this problem leads to a well-posed sparse linear system such that the solution is unique and efficient. Finally, a contextual mesh refining algorithm is applied to ensure topological correspondence. In the experiment, the proposed method is evaluated both qualitatively and quantitatively on two datasets including a publicly available FRGC v2.0 dataset, demonstrating reasonable and reliable correspondence results.

Keywords

3D face Dense correspondence Point set registration 

Notes

Acknowledgement

All correspondences should be forwarded to X. Hu via xiyuan.hu@ia.ac.cn. This work is supported by the National Key R&D Program of China (2017YFC0803505) and the Open Project of National Engineering Laboratory for Forensic Science (2017NELKFKT02).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesHuairouChina
  3. 3.Beijing Visytem Co. LtdHaidianChina

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