3D shape regression for real-time facial animation

  • Authors:
  • Chen Cao;Yanlin Weng;Stephen Lin;Kun Zhou

  • Affiliations:
  • Zhejiang University;Zhejiang University;Microsoft Research Asia;Zhejiang University

  • Venue:
  • ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
  • Year:
  • 2013

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Abstract

We present a real-time performance-driven facial animation system based on 3D shape regression. In this system, the 3D positions of facial landmark points are inferred by a regressor from 2D video frames of an ordinary web camera. From these 3D points, the pose and expressions of the face are recovered by fitting a user-specific blendshape model to them. The main technical contribution of this work is the 3D regression algorithm that learns an accurate, user-specific face alignment model from an easily acquired set of training data, generated from images of the user performing a sequence of predefined facial poses and expressions. Experiments show that our system can accurately recover 3D face shapes even for fast motions, non-frontal faces, and exaggerated expressions. In addition, some capacity to handle partial occlusions and changing lighting conditions is demonstrated.