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

数据驱动的数字娱乐最新进展专辑 | Updated：2022-06-06

- Statistical learning based facial animation
- Statistical learning based facial animation
- 信息与电子工程前沿（英文） 2013年14卷第7期页码：542-550
- 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：
  
  Project supported by the 2013 Annual Beijing Technological and Cultural Fusion for Demonstrated Base Construction and Industrial Nurture (No. Z131100000113007), and the National Natural Science Foundation of China (Nos. 61202324, 61271431, and 61271430)
- DOI：10.1631/jzus.CIDE1307
  中图分类号： TP391.9
- 收稿：2012-12-29，
  
  修回：2013-06-06，
  
  纸质出版：2013-07
- Accepted：
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Statistical learning based facial animation[J]. 信息与电子工程前沿（英文）, 2013,14(7):542-550.

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.
Statistical learning based facial animation[J]. 信息与电子工程前沿（英文）, 2013,14(7):542-550. DOI： 10.1631/jzus.CIDE1307.

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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