Data-driven facial animation via semi-supervised local patch alignment

Jian Zhang, Jun Yu, Jia You, Dapeng Tao, Na Li, Jun Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

48 Citations (Scopus)


This paper reports a novel data-driven facial animation technique which drives a neutral source face to get the expressive target face using a semi-supervised local patch alignment framework. We define the local patch and assume that there exists a linear transformation between a patch of the target face and the intrinsic embedding of the corresponding patch of the source face. Based on this assumption, we compute the intrinsic embeddings of source patches and align these embeddings to form the result. During the course of alignment, we use a set of motion data as shape regularizer to impel the result to approach the unknown target face. The intrinsic embedding can be computed through both locally linear embedding and local tangent space alignment. Experimental results indicate that the proposed framework can obtain decent face driving results. Quantitative and qualitative evaluations of the proposed framework demonstrate its superiority to existing methods.
Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalPattern Recognition
Publication statusPublished - 1 Sept 2016


  • Facial animation
  • Global alignment
  • Linear transformation
  • Local patch
  • Manifold

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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