Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2012

Abstract

In this paper, we present a novel geometry video (GV) framework to model and compress 3-D facial expressions. GV bridges the gap of 3-D motion data and 2-D video, and provides a natural way to apply the well-studied video processing techniques to motion data processing. Our framework includes a set of algorithms to construct GVs, such as hole filling, geodesic-based face segmentation, expression-invariant parameterization (EIP), and GV compression. Our EIP algorithm can guarantee the exact correspondence of the salient features (eyes, mouth, and nose) in different frames, which leads to GVs with better spatial and temporal coherence than that of the conventional parameterization methods. By taking advantage of this feature, we also propose a new H.264/AVC-based progressive directional prediction scheme, which can provide further 10%-16% bitrate reductions compared to the original H.264/AVC applied for GV compression while maintaining good video quality. Our experimental results on real-world datasets demonstrate that GV is very effective for modeling the high-resolution 3-D expression data, thus providing an attractive way in expression information processing for gaming and movie industry.

Keywords

3-D facial expression, H264/AVC, expression-invariant parameterization, feature correspondence, geometry video (GV), video compression

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT)

Volume

22

Issue

1

First Page

77

Last Page

90

ISSN

1051-8215

Identifier

10.1109/TCSVT.2011.2158337

Publisher

IEEE

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TCSVT.2011.2158337

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