Proceedings of the 29th annual conference on Computer graphics and interactive techniques
High-Resolution, Real-time 3D Shape Acquisition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 3 - Volume 03
SNR and temporal scalable coding of 3-D mesh sequences using singular value decomposition
Journal of Visual Communication and Image Representation
Improved prediction methods for scalable predictive animated mesh compression
Journal of Visual Communication and Image Representation
Modeling 3D articulated motions with conformal geometry videos (CGVs)
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
Registration and partitioning-based compression of 3-D dynamic data
IEEE Transactions on Circuits and Systems for Video Technology
Time-Varying Mesh Compression Using an Extended Block Matching Algorithm
IEEE Transactions on Circuits and Systems for Video Technology
Modeling and Compressing 3-D Facial Expressions Using Geometry Videos
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we propose a new compression framework for dynamic 3-D facial expressions acquired from structured light based 3-D camera, based on our previous work. Taking advantage of the near-isometric property of human facial expressions, we parameterize the dynamic 3-D faces into an expression-invariant canonical domain, which naturally generates geometry video and allows us to apply the well-studied video compression technique. Then, low rank and sparse decomposition is applied to each dimension (i.e., X, Y and Z, respectively) before the H.264/AVC encoder is employed to separately encode each dimension instead of encoding them as a whole. Experimental results show that the averaged 3-4 dB gain is achieved by the proposed scheme compared with existing algorithms.