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Motion-aware compression and transmission of mesh animation sequences.

Yang, Bailin and Zhang, Luhong and Li, Frederick W.B. and Xiaoheng, Jiang and Zhigang, Deng and Wang, Meng and Xu, Mingliang (2019) 'Motion-aware compression and transmission of mesh animation sequences.', ACM transactions on intelligent systems and technology., 10 (3). p. 25.


With the increasing demand in using 3D mesh data over networks, supporting effective compression and efficient transmission of meshes has caught lots of attention in recent years. This article introduces a novel compression method for 3D mesh animation sequences, supporting user-defined and progressive transmissions over networks. Our motion-aware approach starts with clustering animation frames based on their motion similarities, dividing a mesh animation sequence into fragments of varying lengths. This is done by a novel temporal clustering algorithm, which measures motion similarity based on the curvature and torsion of a space curve formed by corresponding vertices along a series of animation frames. We further segment each cluster based on mesh vertex coherence, representing topological proximity within an object under certain motion. To produce a compact representation, we perform intra-cluster compression based on Graph Fourier Transform (GFT) and Set Partitioning In Hierarchical Trees (SPIHT) coding. Optimized compression results can be achieved by applying GFT due to the proximity in vertex position and motion. We adapt SPIHT to support progressive transmission and design a mechanism to transmit mesh animation sequences with user-defined quality. Experimental results show that our method can obtain a high compression ratio while maintaining a low reconstruction error.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM transactions on intelligent systems and technology
Date accepted:12 November 2018
Date deposited:13 January 2019
Date of first online publication:29 April 2019
Date first made open access:29 April 2019

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