Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2010

Abstract

The significant advances in developing high-speed shape acquisition devices make it possible to capture the moving and deforming objects at video speeds. However, due to its complicated nature, it is technically challenging to effectively model and store the captured motion data. In this paper, we present a set of algorithms to construct geometry videos for 3D facial expressions, including hole filling, geodesic-based face segmentation, and expression-invariant parametrization. Our algorithms are efficient and robust, and can guarantee the exact correspondence of the salient features (eyes, mouth and nose). Geometry video naturally bridges the 3D motion data and 2D video, and provides a way to borrow the well-studied video processing techniques to motion data processing. With our proposed intra-frame prediction scheme based on H.264/AVC, we are able to compress the geometry videos into a very compact size while maintaining the video quality. Our experimental results on real-world datasets demonstrate that geometry video is effective for modeling the high-resolution 3D expression data.

Keywords

3D facial expression, feature correspondence, geometry video, H.264/AVC, motion data, motion data parametrization, video compression

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

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

First Page

591

Last Page

600

ISBN

9781605589336

Identifier

10.1145/1873951.1874010

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/1873951.1874010

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