TagSplat：面向动态网格建模与跟踪的拓扑感知高斯泼溅方法 (TagSplat: Topology-Aware Gaussian Splatting for Dynamic Mesh Modeling and Tracking)

Topology-consistent dynamic model sequences are essential for applications such as animation and model editing. However, existing 4D reconstruction methods face challenges in generating high-quality topology-consistent meshes. To address this, we propose a topology-aware dynamic reconstruction framework based on Gaussian Splatting. We introduce a Gaussian topological structure that explicitly encodes spatial connectivity. This structure enables topology-aware densification and pruning, preserving the manifold consistency of the Gaussian representation. Temporal regularization terms further ensure topological coherence over time, while differentiable mesh rasterization improves mesh quality. Experimental results demonstrate that our method reconstructs topology-consistent mesh sequences with significantly higher accuracy than existing approaches. Moreover, the resulting meshes enable precise 3D keypoint tracking. Project page: https://haza628.github.io/tagSplat/

翻译：拓扑一致性的动态模型序列对于动画和模型编辑等应用至关重要。然而，现有的四维重建方法在生成高质量拓扑一致网格方面面临挑战。为此，我们提出了一种基于高斯泼溅的拓扑感知动态重建框架。我们引入了一种高斯拓扑结构，该结构显式编码空间连通性。该结构支持拓扑感知的致密化与剪枝，从而保持高斯表示的流形一致性。时间正则化项进一步确保了拓扑结构随时间的一致性，而可微分网格光栅化则提升了网格质量。实验结果表明，我们的方法重建的拓扑一致网格序列在精度上显著优于现有方法。此外，所得网格支持精确的三维关键点跟踪。项目页面：https://haza628.github.io/tagSplat/