title: Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation
summary: Recent advancements in dynamic 3D scene reconstruction have shown promising
  results, enabling high-fidelity 3D novel view synthesis with improved temporal
  consistency. Among these, 4D Gaussian Splatting (4DGS) has emerged as an
  appealing approach due to its ability to model high-fidelity spatial and
  temporal variations. However, existing methods suffer from substantial
  computational and memory overhead due to the redundant allocation of 4D
  Gaussians to static regions, which can also degrade image quality. In this
  work, we introduce hybrid 3D-4D Gaussian Splatting (3D-4DGS), a novel framework
  that adaptively represents static regions with 3D Gaussians while reserving 4D
  Gaussians for dynamic elements. Our method begins with a fully 4D Gaussian
  representation and iteratively converts temporally invariant Gaussians into 3D,
  significantly reducing the number of parameters and improving computational
  efficiency. Meanwhile, dynamic Gaussians retain their full 4D representation,
  capturing complex motions with high fidelity. Our approach achieves
  significantly faster training times compared to baseline 4D Gaussian Splatting
  methods while maintaining or improving the visual quality.
paper_authors._id: 682bedb2fdfa3c5de0e86a0d
paper_authors.name: Seungjun Oh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-20T07:20:43.581Z
paper_authors._id: 672b66744efad666d2efb0c8
paper_authors.avatarUrl: /avatars/9c00a67e9d5b74694759849cca32b015.svg
paper_authors.fullname: Oh Seungjun
paper_authors.type: user
paper_authors.user: ohseungjun
paper_authors._id: 682bedb2fdfa3c5de0e86a0e
paper_authors.name: Younggeun Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:28:33.215Z
paper_authors._id: 66a4a1a7d8e85b03deddfa59
paper_authors.avatarUrl: /avatars/56dbec2101717ad9471e08a03ae51f0c.svg
paper_authors.fullname: Young geun Lee
paper_authors.type: user
paper_authors.user: LeeYG
paper_authors._id: 682bedb2fdfa3c5de0e86a0f
paper_authors.name: Hyejin Jeon
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:28:47.259Z
paper_authors._id: 64c0d2f962983511b95c38d6
paper_authors.avatarUrl: /avatars/68d9d3002d7f5d39aa9a7e2a49d25532.svg
paper_authors.fullname: JeonHyejin
paper_authors.type: user
paper_authors.user: Heyjin
paper_authors._id: 682bedb2fdfa3c5de0e86a10
paper_authors.name: Eunbyung Park
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:28:53.220Z
paper_authors._id: 655e0141d36a195f663ee4b0
paper_authors.avatarUrl: /avatars/97bb695ccefdcb2139b94bcae808cf99.svg
paper_authors.fullname: Eunbyung Park
paper_authors.type: user
paper_authors.user: epark
paper_title: Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation
paper_summary: Recent advancements in dynamic 3D scene reconstruction have shown promising
  results, enabling high-fidelity 3D novel view synthesis with improved temporal
  consistency. Among these, 4D Gaussian Splatting (4DGS) has emerged as an
  appealing approach due to its ability to model high-fidelity spatial and
  temporal variations. However, existing methods suffer from substantial
  computational and memory overhead due to the redundant allocation of 4D
  Gaussians to static regions, which can also degrade image quality. In this
  work, we introduce hybrid 3D-4D Gaussian Splatting (3D-4DGS), a novel framework
  that adaptively represents static regions with 3D Gaussians while reserving 4D
  Gaussians for dynamic elements. Our method begins with a fully 4D Gaussian
  representation and iteratively converts temporally invariant Gaussians into 3D,
  significantly reducing the number of parameters and improving computational
  efficiency. Meanwhile, dynamic Gaussians retain their full 4D representation,
  capturing complex motions with high fidelity. Our approach achieves
  significantly faster training times compared to baseline 4D Gaussian Splatting
  methods while maintaining or improving the visual quality.
paper_ai_summary: A new framework, 3D-4D Gaussian Splatting, improves computational efficiency in dynamic 3D scene reconstruction by adaptively representing static and dynamic regions, reducing parameter count and training time.
paper_ai_keywords: 4D Gaussian Splatting 4DGS 3D-4D Gaussian Splatting Gaussians 3D Gaussians 4D Gaussians temporal consistency high-fidelity computational efficiency dynamic elements temporal invariant