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Motion-aware Compression and Transmission of Mesh Animation Sequences

Yang, Bailin; Zhang, Luhong; Li, Frederick W.B.; Xiaoheng, Jiang; Zhigang, Deng; Wang, Meng; Xu, Mingliang

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Bailin Yang

Luhong Zhang

Jiang Xiaoheng

Deng Zhigang

Meng Wang

Mingliang Xu


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.


Yang, B., Zhang, L., Li, F. W., Xiaoheng, J., Zhigang, D., Wang, M., & Xu, M. (2019). Motion-aware Compression and Transmission of Mesh Animation Sequences. ACM Transactions on Intelligent Systems and Technology, 10(3), Article 25.

Journal Article Type Article
Acceptance Date Nov 12, 2018
Online Publication Date Apr 29, 2019
Publication Date May 31, 2019
Deposit Date Jan 8, 2019
Publicly Available Date Jan 13, 2019
Journal ACM Transactions on Intelligent Systems and Technology
Print ISSN 2157-6904
Electronic ISSN 2157-6904
Publisher Association for Computing Machinery (ACM)
Peer Reviewed Peer Reviewed
Volume 10
Issue 3
Article Number 25


Accepted Journal Article (2 Mb)

Copyright 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

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