2004
DOI: 10.1016/j.cag.2003.10.002
|View full text |Cite
|
Sign up to set email alerts
|

Compression of soft-body animation sequences

Abstract: We describe a compression scheme for the geometry component of 3D animation sequences. This scheme is based on the principle component analysis (PCA) method, which represents the animation sequence using a small number of basis functions. Second-order linear prediction coding (LPC) is applied to the PCA coefficients in order to further reduce the code size by exploiting the temporal coherence present in the sequence. Our results show that applying LPC to the PCA scheme results in significant performance improv… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
170
0

Year Published

2005
2005
2013
2013

Publication Types

Select...
5
2
1

Relationship

0
8

Authors

Journals

citations
Cited by 192 publications
(172 citation statements)
references
References 19 publications
1
170
0
Order By: Relevance
“…A metric used quite commonly in dynamic mesh compression is the KG error, proposed by Karni and Gotsman in [KG04]. The metric is designed specifically for animated triangle meshes.…”
Section: Kg Errormentioning
confidence: 99%
See 1 more Smart Citation
“…A metric used quite commonly in dynamic mesh compression is the KG error, proposed by Karni and Gotsman in [KG04]. The metric is designed specifically for animated triangle meshes.…”
Section: Kg Errormentioning
confidence: 99%
“…It works on matrices describing original and distorted meshes, where columns of the matrices describe trajectories of respective vertices of the animation. Having a matrix M describing the original animation sequence, and a matrix M describing the distorted version, the metric uses the Frobenius norm of the matrix difference M − M and produces a normalised version (for details see [KG04]) of this value as the result. Therefore, having function AMSE(M, M ) that computes the average mean squared error between animations represented by matrices M and M , the KG error can be rewritten in the form of function KG(M, M ) = f (M, AMSE(M, M )).…”
Section: Kg Errormentioning
confidence: 99%
“…Animation compression approaches have been explored in the last half decade [Lengyel 1999;Briceño et al 2003;Guskov and Khodakovsky 2004], including PCA compression of shapes [Sloan et al 2001], animations [Alexa and Müller 2000;Karni and Gotsman 2004], and parameterized animation compression [Hakura et al 2000]. The goal of our approach is not animation compression per se, but rather hardware-accelerated rendering using a very simple and common vertex shader technique called matrix palette skinning [Lindholm et al 2001].…”
Section: Related Workmentioning
confidence: 99%
“…min(4, B). We report approximation errors in terms of percent distortion, E = 100% * P − P approx F / P − P timeAverage F (as in [Karni and Gotsman 2004] Figure 11). For comparison, we plot (blue curve) the PCA approximation of Alexa and Müller [2000] (B=1 flexible bone) which requires much higher rank to achieve similar accuracy, e.g., rank 37 (B = 1) is comparable to the rank 7 (B = 22), NNLS, flexible bone approximation shown in Figure 11.…”
Section: Comparison To Vertex Buffer Objectsmentioning
confidence: 99%
“…(1) E 1 : average norm-1 error (in terms of mm) of vertex position, (2) E 2 : KG error [9] (a well-known measurement in computer graphics), Note that each data point of different bit rate is obtained by varying the coordinate accuracy (BPV=8,9,10,12,14 bit per vertex) for MPEG-4 AFX 3DMC or varying residual accuracy (Quality Factor, QF=1,2,5,10, 24, the larger, the more accuracy) for the modified 3DMC.…”
Section: Experiments Resultsmentioning
confidence: 99%