Cart (Loading....) | Create Account
Close category search window
 

Learning and synthesizing MPEG-4 compatible 3-D face animation from video sequence

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Wen Gao ; Inst. of Comput. Technol., Chinese Acad. of Sci., Beijing, China ; Yiqiang Chen ; Rui Wang ; Shiguang Shan
more authors

We present a new system that applies an example-based learning method to learn facial motion patterns from a video sequence of individual facial behavior such as lip motion and facial expressions, and using that to create vivid three-dimensional (3-D) face animation according to the definition of MPEG-4 face animation parameters. The system consists of three key modules, face tracking, pattern learning, and face animation. In face tracking, to reduce the complexity of the tracking process, a novel coarse-to-fine strategy combined with a Kalman filter is proposed for localizing key facial landmarks in each image of the video. The landmarks' sequence is normalized into a visual feature matrix and then fed to the next step of process. In pattern learning, in the pretraining stage, the parameters of the camera that took the video are requested with the training video data so the system can estimate the basic mapping from a normalized two-dimensional (2-D) visual feature matrix to the representation in 3-D MPEG-4 face animation parameter space, in assistance with the computer vision method. In the practice stage, considering that in most cases camera parameters are not provided with video data, the system uses machine learning technology to complement the incomplete 3-D information for the mapping that information is needed in face orientation presentation. The example-based learning in this system integrates several methods including clustering, HMM, and ANN to make a better conversion from a 2-D to 3-D model and better estimation of incomplete 3-D information for good mapping; this will be used to drive face animation thereafter. In face animation, the system can synthesize face animation following any type of face motion in video. Experiments show that our system produces more vivid face motion animation, compared to other early systems.

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:13 ,  Issue: 11 )

Date of Publication:

Nov. 2003

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.