Statistical learning based facial animation

XU Shibiao; MA Guanghui; MENG Weiliang; ZHANG Xiaopeng

doi:10.1631/jzus.CIDE1307

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Statistical learning based facial animation

Special Issue on the Recent Advances of Data-Driven Digital Entertainment | Updated：2022-06-06

- Statistical learning based facial animation
- Statistical learning based facial animation
- Frontiers of Information Technology & Electronic Engineering Vol. 14, Issue 7, Pages: 542-550(2013)
- Affiliations：
  
  National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Author bio：
  
  [ "Shibiao XU, E-mail: aquathinker@gmail.com" ]
  Xiaopeng ZHANG, E-mail: xpzhang@nlpr.ia.ac.cn
- Funds：
- DOI：10.1631/jzus.CIDE1307
  CLC： TP391.9
- Received：29 December 2012，
  
  Revised：2013-06-06，
  
  Published：2013-07
- Accepted：
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XU Shibiao, MA Guanghui, MENG Weiliang, et al. Statistical learning based facial animation[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(7): 542-550.
DOI：

XU Shibiao, MA Guanghui, MENG Weiliang, et al. Statistical learning based facial animation[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(7): 542-550. DOI： 10.1631/jzus.CIDE1307.

摘要

Abstract

To synthesize real-time and realistic facial animation

we present an effective algorithm which combines image- and geometry-based methods for facial animation simulation. Considering the numerous motion units in the expression coding system

we present a novel simplified motion unit based on the basic facial expression

and construct the corresponding basic action for a head model. As image features are difficult to obtain using the performance driven method

we develop an automatic image feature recognition method based on statistical learning

and an expression image semi-automatic labeling method with rotation invariant face detection

which can improve the accuracy and efficiency of expression feature identification and training. After facial animation redirection

each basic action weight needs to be computed and mapped automatically. We apply the blend shape method to construct and train the corresponding expression database according to each basic action

and adopt the least squares method to compute the corresponding control parameters for facial animation. Moreover

there is a pre-integration of diffuse light distribution and specular light distribution based on the physical method

to improve the plausibility and efficiency of facial rendering. Our work provides a simplification of the facial motion unit

an optimization of the statistical training process and recognition process for facial animation

solves the expression parameters

and simulates the subsurface scattering effect in real time. Experimental results indicate that our method is effective and efficient

and suitable for computer animation and interactive applications.

关键词

Keywords

references

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