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International audience; With the recent advances in data acquisition techniques, the compression of various dynamic mesh sequence data has become an important topic in the computer graphics community. In this article, we present a new spatio-temporal segmentation-based approach for the adaptive compression of the dynamic mesh sequences. Given an input dynamic mesh sequence, we first compute an initial temporal cut to obtain a small subsequence by detecting the temporal boundary of dynamic behavior. Then, we apply a two-stage vertex clustering on the resulting subsequence to classify the vertices into groups with optimal intra-affinities. After that, we design a temporal segmentation step based on the variations of the principal components within each vertex group prior to performing a PCA-based compression. Furthermore, we apply an extra step on the lossless compression of the PCA bases and coefficients to gain more storage saving. Our approach can adaptively determine the temporal and spatial segmentation boundaries to exploit both temporal and spatial redundancies. We have conducted extensive experiments on different types of 3D mesh animations with various segmentation configurations. Our comparative studies show the advantages of our approach for the compression of 3D mesh animations.

Date of Conference: 9-11 April 2009 Conference Name: IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009 In the last few years, there is great increase in capture and representation of real 3-Dimensonal scenes using 3D animation models. The 3D signals are then compressed, transmitted to the client side and reconstructed for the user view. Each step mentioned here opened a new subject in the field of signal processing. While processing these models, using the model as a whole is not the best approach. Therefore clustering the model vertices became a very common method. For example, it is very common to use motion based clustering in animation compression. In this paper a new dynamic model clustering algorithm is proposed. Animation vertices are first put through PCA and partitioned into their eigenvalues and eigenvectors. The eigenvectors found using the proposed method can be called eigentrajectories. Then the dot product of the these eigentrajectories with the trajectories of the animation vertice are found. These coefficients are used to cluster the animation model. The results and the comparisons with a similar approach show that the proposed algorithm is successful.

International audience; This paper presents a new compression technique for 3D dynamic meshes, referred to as FAMC - Frame-based Animated Mesh Compression, recently promoted within the MPEG-4 standard as Amendement 2 of part 16 AFX (Animation Framework eXtension). The FAMC approach combines a model-based motion compensation strategy, with transform/predictive coding of residual errors. First, a skinning motion compensation model is automatically computed from a frame-based representation and then encoded. Subsequently, either 1) DCT/lifting wavelets or 2) layer-based predictive coding is employed to exploit remaining spatio-temporal correlations in the residual signal. The proposed encoder offers high compression performances (gains in bit rate of 60% with respect to the previous MPEG-4 technique and of 20% to 40% with respect to state-of-the-art approaches) and is well suited for compressing both geometric and photometric (normal vectors, colors...) attributes. In addition, the FAMC method supports a rich set of functionalities including streaming, scalability (spatial, temporal and quality) and progressive transmission

International audience; This paper presents a new compression technique for 3D dynamic meshes, referred to as FAMC - Frame-based Animated Mesh Compression, recently promoted within the MPEG-4 standard as Amendement 2 of part 16 (AFX - Animation Framework eXtension). The FAMC approach combines a model-based motion-compensation strategy with transform/predictive coding of residual errors. First, a skinning motion-compensation model is automatically derived from a frame-based representation. Subsequently, either 1) DCT/lifting wavelets or 2) layer-based predictive coding is employed to exploit remaining spatio-temporal correlations in the residual signal. Both motion model parameters and residual signal components are finally encoded by using context-based adaptive binary arithmetic coding (CABAC). The proposed FAMC encoder offers high compression performance with gains of 60% in terms of bit-rate savings relative to previous MPEG-4 technology and of 20% to 40% relative to state-of-the-art techniques. FAMC is well suited for compressing both geometric and photometric (normal vectors, colors...) attributes. In addition, FAMC also supports a rich set of functionalities including streaming, scalability (spatial, temporal and quality) and progressive transmission

The MPEG-4 standard adopted a novel technology for compression of dynamic 3D meshes with constant connectivity and time-varying geometry, referred to as FAMC - frame-based animated mesh compression. In this paper, we propose two optimizations of the FAMC approach, aiming at improving the compression efficiency. The first one is based on a PCA (principal component analysis) decomposition of the motion compensation error residuals. The second improves the bi-orthogonal (4-2) wavelet coding approach supported by the standard, by introducing an optimal bit allocation procedure, combined with an adapted quantization of wavelet coefficients. Experimental results show that both optimizations lead to significant gains in compression rate (about 20-30%) at low bitrates.