Publication Type

Conference Proceeding Article

Publication Date

10-2010

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.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

MM'10: Proceedings of the 18th ACM International Conference on Multimedia, Firenze, Italy, October 25-29, 2010

First Page

591

Last Page

600

ISBN

9781605589336

Identifier

10.1145/1873951.1874010

Publisher

ACM

City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1145/1873951.1874010

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