Predictive compression of animated 3D models by optimized weighted blending of key‐frames

Abstract

Efficient compression techniques are required for animated mesh sequences with fixed connectivity and time‐varying geometry. In this paper, we propose a key‐frame‐based technique for three‐dimensional dynamic mesh compression. First, key‐frames are extracted from the animated sequence. Extracted key‐frames are then linearly combined using blending weights to predict the vertex locations of the other frames. These blending weights play a key role in the proposed algorithm because the prediction performance and the required number of key‐frames greatly depend on these weights. We present a novel method in order to compute the optimum blending weight that makes it possible to predict location of the vertices of the non‐key frames with the minimum number of key‐frames. The residual prediction errors are finally quantized and encoded using Huffman coding and another heuristic method. Experimental results on different test sequences with various sizes, topologies, and geometries demonstrate the privileged performance of the proposed method compared with the previous techniques. Copyright © 2015 John Wiley & Sons, Ltd.

Spatio-temporal Segmentation Based Adaptive Compression of Dynamic Mesh Sequences

“…Temporal segmentation has been exploited for the compression of motion capture data [16,17,44,63], but the efficiencies of these methods for 3D mesh animation compression may be significantly decreased since 3D mesh surfaces typically have much more dense vertices and additional topology than motion capture data [38]. Given a mesh sequence, after partitioning the sequence into clusters with similar poses, researchers either apply PCA to compress each group to achieve the optimal compression ratio [36] or extract a key-frame of each cluster and encode the rest frames as the blending weights of the extracted key-frames [19]. Similarly, in [7], Chen et al apply the manifold harmonic bases to characterize the primary poses (key-frames) and the deformation transfer technique to recover the geometric details of each frame within a cluster.…”