Time-varying meshes, characterized by dynamic connectivity and varying vertex counts, hold significant promise for applications such as augmented reality. However, their practical utilization remains challenging due to the substantial data volume required for high-fidelity representation. While various compression methods attempt to leverage temporal redundancy between consecutive mesh frames, most struggle with topological inconsistency and motion-induced artifacts. To address these issues, we propose Time-Varying Mesh Compression (TVMC), a novel framework built on multi-stage coarse-to-fine anchor mesh generation for inter-frame prediction. Specifically, the anchor mesh is progressively constructed in three stages: initial, coarse, and fine. The initial anchor mesh is obtained through fast topology alignment to exploit temporal coherence. A Kalman filter-based motion estimation module then generates a coarse anchor mesh by accurately compensating inter-frame motions. Subsequently, a Quadric Error Metric-based refinement step optimizes vertex positions to form a fine anchor mesh with improved geometric fidelity. Based on the refined anchor mesh, the inter-frame motions relative to the reference base mesh are encoded, while the residual displacements between the subdivided fine anchor mesh and the input mesh are adaptively quantized and compressed. This hierarchical strategy preserves consistent connectivity and high-quality surface approximation, while achieving an efficient and compact representation of dynamic geometry. Extensive experiments on standard MPEG dynamic mesh sequences demonstrate that TVMC achieves state-of-the-art compression performance. Compared to the latest V-DMC standard, it delivers a significant BD-rate gain of 10.2% ~ 16.9%, while preserving high reconstruction quality. The code is available at https://github.com/H-Huang774/TVMC.

翻译：时变网格具有动态连接性和可变顶点数量的特点，在增强现实等应用中展现出巨大潜力。然而，由于高保真表示所需的数据量巨大，其实际应用仍面临挑战。尽管多种压缩方法尝试利用连续网格帧之间的时间冗余性，但大多数方法难以处理拓扑结构不一致性和运动引起的伪影问题。为解决这些问题，我们提出了时变网格压缩（TVMC）——一种基于多阶段由粗到精锚点网格生成的新型帧间预测框架。具体而言，锚点网格通过三个阶段逐步构建：初始阶段、粗粒度阶段和精细阶段。初始锚点网格通过快速拓扑对齐获得，以利用时间相干性。随后，基于卡尔曼滤波器的运动估计模块通过精确补偿帧间运动生成粗粒度锚点网格。接着，基于二次误差度量的优化步骤调整顶点位置，形成具有更高几何保真度的精细锚点网格。基于优化后的锚点网格，对相对于参考基础网格的帧间运动进行编码，同时对细分后的精细锚点网格与输入网格之间的残差位移进行自适应量化和压缩。这种分层策略在保持连接一致性和高质量表面逼近的同时，实现了动态几何的高效紧凑表示。在标准MPEG动态网格序列上的大量实验表明，TVMC实现了最先进的压缩性能。与最新的V-DMC标准相比，该方法在保持高重建质量的同时，取得了10.2% ~ 16.9%的显著BD-rate增益。代码已开源：https://github.com/H-Huang774/TVMC。