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.

中文翻译：

---

使用局部光谱分析和变形传递压缩具有精细细节的动画网格

以几何为中心的形状动画，通常表示为具有固定连接和时间变形几何的动态网格，在数字娱乐和其他相关图形应用中变得无处不在。然而，具有精细细节的数字动画需要网格几何体上更多的纹理多样性，总是消耗大量的存储空间，并且紧凑地存储和有效传输这些网格在技术上仍然具有挑战性。在本文中，我们提出了一种新的基于关键帧的动态网格压缩方法，其中我们通过应用分段流形谐波基来减少主要姿势的时空冗余，将网格分解为低频和高频部分并通过使用变形转移来恢复高频细节。首先，我们将动画网格划分为几个姿势相似的集群，每个集群中网格的主要姿势可以表征为从该集群的关键帧导出的流形谐波基的线性组合。其次，我们使用从关键帧重建细节的变形转移技术恢复每个主要姿势的几何细节。因此，我们只需要存储非常少量的关键帧和一些用于压缩时变网格的谐波系数，与显式存储基的传统方法相比，这将减少大量存储。最后，我们采用最先进的静态网格压缩方法来存储关键帧，并对谐波系数应用二阶线性预测编码，以进一步减少时空冗余。我们对各种数据集的综合实验和全面评估表明，我们的新方法可以获得高压缩比，同时保留高保真几何细节并同时保证有限的人类感知失真率，正如流行的 Karni-Gotsman 错误和我们的新设计的局部刚性误差度量。