LBF Based 3D Regression for Facial Animation

This paper presents a system for performance-driven avatar animation by estimating facial pose and expression parameters from single image. In this system, a 3D shape prediction model is trained based on local binary feature (LBF) algorithm, which use random forest to extract image features and lear...

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Bibliographic Details
Published in2016 International Conference on Virtual Reality and Visualization (ICVRV) pp. 276 - 279
Main Authors Congquan Yan, Liang-Hao Wang, Jianing Li, Dong-Xiao Li, Ming Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Animation
avatar animation
Avatars
Face
face tracking
facial performance
Feature extraction
LBF regression
Shape
Three-dimensional displays
Two dimensional displays
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Summary:This paper presents a system for performance-driven avatar animation by estimating facial pose and expression parameters from single image. In this system, a 3D shape prediction model is trained based on local binary feature (LBF) algorithm, which use random forest to extract image features and learns a linear regression model mapping these features to 3D shape. With the help of this model, the 3D positions of facial vertexes can be estimated from a web camera image. The facial pose and expression parameters can be calculated by solving an optimization problem that fitting a set of blend shapes models to these 3D vertexes. Experiments show that our system can estimate accurate facial parameters from single image and generate similar looking avatar animation.
DOI:10.1109/ICVRV.2016.52