计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 198-201.

• 模式识别与图像处理 • 上一篇    下一篇

基于线索引导的面部运动捕捉与数据虚拟重用方法

张超,金龙斌,韩成   

  1. 长春理工大学计算机科学技术学院 长春130022
  • 出版日期:2018-06-20 发布日期:2018-08-03
  • 作者简介:张 超(1985-),男,博士,讲师,CCF会员,主要研究方向为图像处理、计算机视觉、增强现实等,E-mail:zhangchao@cust.edu.cn;金龙斌(1991-),男,硕士生,主要研究方向为虚拟现实与多媒体技术;韩 成(1978-),男,博士,副教授,主要研究方向为数字媒体与虚拟现实,E-mail:hancheng@cust.edu.cn(通信作者)。
  • 基金资助:
    吉林省科技攻关计划项目(20170203003GX),吉林省科技攻关计划项目(20170203004GX)资助

Method of Facial Motion Capture and Data Virtual Reusing Based on Clue Guided

ZHANG Chao,JIN Long-bin,HAN Cheng   

  1. School of Computer Science and Technology,Changchun University of Science and Technology,Changchun 130022,China
  • Online:2018-06-20 Published:2018-08-03

摘要: 多数无标记面部表情运动捕捉方法仅捕捉面部表情运动的平面位置变化,而无纵深变化描述。针对这一问题,提出了基于线索引导的面部运动捕捉与数据虚拟重用方法。首先,使用单目视觉系统对人脸主体区域进行定位;然后,采用级联回归模型对人脸地标特征逐步求精,通过利用人脸特征的主动地标线索和地标特征的深度线索得到特征点在三维空间上的位置变换关系;最后,借助面部骨骼节点的数据表示实现面部表情运动重构。通过在线的实时面部表情运动捕捉实验效果可以看出,该方法不仅能够实现不同视角下对应地标特征的精确匹配,而且能够较好地将真实面部运动赋予虚拟角色。

关键词: 级联回归模型, 面部运动捕捉, 人脸特征, 线索引导

Abstract: Most of the unmarked facial expression motion capture methods only capture the planar position changes of facial expression movements,have no description of the depth changes.For this problem,a clue guided based facial motion captures and data virtual reusing method was proposed.Firstly,the method uses a monocular vision system for locating the main body of the face.Then,the face landmark features are refined by the cascade regression model.We can obtain the positional transformation relationship of the feature points in the three-dimensional space by the active landmark cues of the facial features and the depth clues of the landmark features.Finally,the facial skeleton node data are used to achieve facial expression motions’ reconstruction.Through the experiment of the online real-time facial expression motion capture,it can be seen that this method can not only achieve the exact match of the corresponding landmark features in different perspectives,but can also better assigns real facial motion to virtual characters.

Key words: Cascade regression model, Clue guided, Facial feature, Facial motion capture

中图分类号: 

  • TP391
[1]CHAN J C P,LEUNG H,TANG J K T,et al.A Virtual Reality Dance Training System Using Motion Capture Technology[J].IEEE Transactions on Learning Technologies,2011,4(2):187-195.
[2]DARUJATI C,HARIADI M.Facial motion capture with 3D active appearance models[C]∥International Conference on Instrumentation,Communications,Information Technology,and Biomedical Engineering.IEEE,2013:59-64.
[3]周芳芳,赵颖,杨斌.拟人Agent面部复杂运动合成的研究与实现[J].计算机科学,2012,39(5):195-197.
[4]COOTES T F,IONITA M C,LINDNER C,et al.Robust and accurate shape model fitting using random forest regression vo-ting[C]∥European Conference on Computer Vision.Springer Berlin Heidelberg,2012:278-291.
[5]LIN S D,LIU B F,LIN J H.Combining speeded-up robust features with principal component analysis in face recognition system[J].International Journal of Innovative Computing Information & Control Ijicic,2012,8(12):8545-8558.
[6]王绍宇.基于小波和ICA的面部特征定位[J].计算机科学,2006,33(9):199-200.
[7]TZIMIROPOULOS G.Project-Out Cascaded Regression with an application to face alignment[C]∥Computer Vision and Pattern Recognition.IEEE,2015:3659-3667.
[8]TU C T,LIEN J J.Automatic location of facial feature points and synthesis of facial sketches using direct combined model[J].IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society,2010,40(4):1158-1169.
[9]XIONG X,TORRE F D L.Supervised Descent Method and Its Applications to Face Alignment[J].Computer Vision & Pattern Recognition,2013,9(4):532-539.
[10]SUN Y,WANG X G,TANG X O.Deep Convolutional Network Cascade for Facial Point Detection[C]∥Computer Vision and Pattern Recognition.IEEE,2013:3476-3483.
[11]LI Y Q,WANG S F,ZHAO Y P,et al.Simultaneous facial feature tracking and facial expression recognition[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2013,22(7):2559-2573.
[12]贾静平,覃亦华.基于深度学习的视觉跟踪算法研究综述[J].计算机科学,2017,44(s1):19-23.
[13]牛耕田,王昌明,孟红波.基于多尺度稀疏表示的面部疲劳识别[J].计算机科学,2016,43(8):282-285.
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