TagSplat: Topology-Aware Gaussian Splatting for Dynamic Mesh Modeling and Tracking

• URL: http://arxiv.org/abs/2512.01329v1
• Date: Mon, 01 Dec 2025 06:41:54 GMT
• Title: TagSplat: Topology-Aware Gaussian Splatting for Dynamic Mesh Modeling and Tracking
• Authors: Hanzhi Guo, Dongdong Weng, Mo Su, Yixiao Chen, Xiaonuo Dongye, Chenyu Xu,
• Abstract summary: Topology-consistent dynamic model sequences are essential for applications such as animation and model editing.<n>We propose a topology-aware dynamic reconstruction framework based on Gaussian Splatting.<n> Experimental results demonstrate that our method reconstructs topology-consistent mesh sequences with significantly higher accuracy than existing approaches.
• Score: 5.617341252682462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/

Related papers

• TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction [16.246538335191982]
  Tomographic Geometry Field (TG-Field) is a geometry-aware Gaussian deformation framework for computed tomography (CT) reconstruction.<n> TG-Field consistently outperforms existing methods, achieving state-of-the-art reconstruction accuracy under highly sparse-view conditions.
  arXiv  Detail & Related papers  (2026-02-12T08:33:01Z)
• GaussianMorphing: Mesh-Guided 3D Gaussians for Semantic-Aware Object Morphing [31.47036497656664]
  We introduce a novel framework for semantic-aware 3D shape and texture morphing from multi-view images.<n>The core of our framework is a unified deformation strategy that anchors 3DGaussians to reconstructed mesh patches.<n>On our proposed TexMorph benchmark, GaussianMorphing substantially outperforms prior 2D/3D methods.
  arXiv  Detail & Related papers  (2025-10-02T14:07:55Z)
• Laplacian Analysis Meets Dynamics Modelling: Gaussian Splatting for 4D Reconstruction [9.911802466255653]
  We propose a novel dynamic 3DGS framework with hybrid explicit-implicit functions.<n>Our method demonstrates state-of-the-art performance in reconstructing complex dynamic scenes, achieving better reconstruction fidelity.
  arXiv  Detail & Related papers  (2025-08-07T01:39:29Z)
• GauSTAR: Gaussian Surface Tracking and Reconstruction [9.017056233547084]
  GauSTAR is a novel method that achieves photo-realistic rendering, accurate surface reconstruction, and reliable 3D tracking.<n>Our method effectively tracks and reconstructs dynamic surfaces, enabling a range of applications.
  arXiv  Detail & Related papers  (2025-01-17T16:26:24Z)
• Dynamic Gaussians Mesh: Consistent Mesh Reconstruction from Dynamic Scenes [27.531394287148384]
  We introduce Dynamic Gaussians Mesh (DG-Mesh), a framework to reconstruct a high-fidelity and time-consistent mesh from dynamic input.<n>Our work leverages the recent advancement in 3D Gaussian Splatting to construct the mesh sequence with temporal consistency from dynamic observations.<n>We introduce the Gaussian-Mesh Anchoring, which encourages evenly distributed Gaussians, resulting better mesh reconstruction through mesh-guided densification and pruning on the deformed Gaussians.
  arXiv  Detail & Related papers  (2024-04-18T17:58:16Z)
• Mesh-based Gaussian Splatting for Real-time Large-scale Deformation [58.18290393082119]
  It is challenging for users to directly deform or manipulate implicit representations with large deformations in the real-time fashion. We develop a novel GS-based method that enables interactive deformation. Our approach achieves high-quality reconstruction and effective deformation, while maintaining the promising rendering results at a high frame rate.
  arXiv  Detail & Related papers  (2024-02-07T12:36:54Z)
• Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis [66.49788145564004]
  We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology. Our methods perform favorably against the current state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-12-17T15:05:25Z)
• Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshes [52.038346313823524]
  This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology. Our method is able to produce high-quality meshes, particularly with diverse topologies, as compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-10T08:32:57Z)
• Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases [131.50372468579067]
  We propose a method for unsupervised reconstruction of a temporally-consistent sequence of surfaces from a sequence of time-evolving point clouds. We represent the reconstructed surfaces as atlases computed by a neural network, which enables us to establish correspondences between frames. Our approach outperforms state-of-the-art ones on several challenging datasets.
  arXiv  Detail & Related papers  (2021-11-12T17:48:25Z)
• Primal-Dual Mesh Convolutional Neural Networks [62.165239866312334]
  We propose a primal-dual framework drawn from the graph-neural-network literature to triangle meshes. Our method takes features for both edges and faces of a 3D mesh as input and dynamically aggregates them. We provide theoretical insights of our approach using tools from the mesh-simplification literature.
  arXiv  Detail & Related papers  (2020-10-23T14:49:02Z)
• Dense Non-Rigid Structure from Motion: A Manifold Viewpoint [162.88686222340962]
  Non-Rigid Structure-from-Motion (NRSfM) problem aims to recover 3D geometry of a deforming object from its 2D feature correspondences across multiple frames. We show that our approach significantly improves accuracy, scalability, and robustness against noise.
  arXiv  Detail & Related papers  (2020-06-15T09:15:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.