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Dynamic Gaussians Mesh: Consistent Mesh Reconstruction from Dynamic Scenes

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arxiv 2404.12379 v3 pith:UJORCRLY submitted 2024-04-18 cs.CV

classification cs.CV
keywords meshdynamicgaussiansdg-meshgaussianreconstructiontimebetter
verification ladder T0 review T1 audit T2 compute T3 formal
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Modern 3D engines and graphics pipelines require mesh as a memory-efficient representation, which allows efficient rendering, geometry processing, texture editing, and many other downstream operations. However, it is still highly difficult to obtain high-quality mesh in terms of detailed structure and time consistency from dynamic observations. To this end, we introduce Dynamic Gaussians Mesh (DG-Mesh), a framework to reconstruct a high-fidelity and time-consistent mesh from dynamic input. Our work leverages the recent advancement in 3D Gaussian Splatting to construct the mesh sequence with temporal consistency from dynamic observations. Building on top of this representation, DG-Mesh recovers high-quality meshes from the Gaussian points and can track the mesh vertices over time, which enables applications such as texture editing on dynamic objects. 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. By applying cycle-consistent deformation between the canonical and the deformed space, we can project the anchored Gaussian back to the canonical space and optimize Gaussians across all time frames. During the evaluation on different datasets, DG-Mesh provides significantly better mesh reconstruction and rendering than baselines. Project page: https://www.liuisabella.com/DG-Mesh

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Cited by 5 Pith papers

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    FutureSurf, a new benchmark for held-out future surface reconstruction, shows deformation-MLP methods leave a 2-6.6× future-surface gap while rendering quality stays flat.

  3. Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments

    cs.LG 2026-01 conditional novelty 6.0 of 10

    Flow equivariant world models use a latent memory that shifts with the agent and with inferred object motion, giving stable long-horizon prediction under partial observability.

  4. TRACE: Learning 3D Gaussian Physical Dynamics from Multi-view Videos

    cs.CV 2025-08 conditional novelty 6.0 of 10

    TRACE predicts future frames of dynamic 3D scenes by learning a per-particle translation-rotation dynamics system inside 3D Gaussian Splatting, without labels.

  5. CharacterShot: Controllable and Consistent 4D Character Animation

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A new pipeline generates pose-controlled, view-consistent 4D character animations from one reference image and a 2D pose sequence, backed by a new 13,115-character dataset and benchmark.

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