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Compressing animated meshes with fine details using local spectral analysis and deformation transfer

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Abstract

Geometry-centric shape animation, usually represented as dynamic meshes with fixed connectivity and time-deforming geometry, is becoming ubiquitous in digital entertainment and other relevant graphics applications. However, digital animation with fine details, which requires more diversity of texture on meshed geometry, always consumes a significant amount of storage space, and compactly storing and efficiently transmitting these meshes still remain technically challenging. In this paper, we propose a novel key-frame-based dynamic meshes compression method, wherein we decompose the meshes into the low-frequency and high-frequency parts by applying piece-wise manifold harmonic bases to reduce spatial-temporal redundancy of primary poses and by using deformation transfer to recover high-frequency details. First of all, we partition the animated meshes into several clusters with similar poses, and the primary poses of meshes in each cluster can be characterized as a linear combination of manifold harmonic bases derived from the key-frame of that cluster. Second, we recover the geometric details on each primary pose using the deformation transfer technique which reconstructs the details from the key-frames. Thus, we only need to store a very small number of key-frames and a few harmonic coefficients for compressing time-varying meshes, which would reduce a significant amount of storage in contrast with traditional methods where bases were stored explicitly. Finally, we employ the state-of-the-art static mesh compression method to store the key-frames and apply a second-order linear prediction coding to the harmonics coefficients to further reduce the spatial-temporal redundancy. Our comprehensive experiments and thorough evaluations on various datasets have manifested that, our novel method could obtain a high compression ratio while preserving high-fidelity geometry details and guaranteeing limited human perceived distortion rate simultaneously, as quantitatively characterized by the popular Karni–Gotsman error and our newly devised local rigidity error metrics.

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Acknowledgements

This research is supported in part by National Natural Science Foundation of China (Nos. 61672077, 61672149 and 61532002), National Key R&D Program of China (No. 2017YFF0106407), Applied Basic Research Program of Qingdao (No. 161013xx), National Science Foundation of USA (Nos. IIS-0949467, IIS-1047715, IIS-1715985, and IIS-1049448), the capital health research and development of special 2016-1-4011, and the Excellence Foundation of BUAA for PhD Students (No. 2017043).

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  1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China

    Chengju Chen, Qing Xia, Shuai Li & Aimin Hao

  2. Department of Computer Science, Stony Brook University, New York, USA

    Hong Qin

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  1. Chengju Chen
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  2. Qing Xia
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Correspondence to Shuai Li.

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An earlier and shorter version of this paper was presented at the Computer Graphics International Conference 2018 [4].

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Chen, C., Xia, Q., Li, S. et al. Compressing animated meshes with fine details using local spectral analysis and deformation transfer. Vis Comput 36, 1029–1042 (2020). https://doi.org/10.1007/s00371-019-01650-5

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