title: MoDec-GS: Global-to-Local Motion Decomposition and Temporal Interval
    Adjustment for Compact Dynamic 3D Gaussian Splatting
summary: 3D Gaussian Splatting (3DGS) has made significant strides in scene
  representation and neural rendering, with intense efforts focused on adapting
  it for dynamic scenes. Despite delivering remarkable rendering quality and
  speed, existing methods struggle with storage demands and representing complex
  real-world motions. To tackle these issues, we propose MoDecGS, a
  memory-efficient Gaussian splatting framework designed for reconstructing novel
  views in challenging scenarios with complex motions. We introduce GlobaltoLocal
  Motion Decomposition (GLMD) to effectively capture dynamic motions in a
  coarsetofine manner. This approach leverages Global Canonical Scaffolds (Global
  CS) and Local Canonical Scaffolds (Local CS), extending static Scaffold
  representation to dynamic video reconstruction. For Global CS, we propose
  Global Anchor Deformation (GAD) to efficiently represent global dynamics along
  complex motions, by directly deforming the implicit Scaffold attributes which
  are anchor position, offset, and local context features. Next, we finely adjust
  local motions via the Local Gaussian Deformation (LGD) of Local CS explicitly.
  Additionally, we introduce Temporal Interval Adjustment (TIA) to automatically
  control the temporal coverage of each Local CS during training, allowing
  MoDecGS to find optimal interval assignments based on the specified number of
  temporal segments. Extensive evaluations demonstrate that MoDecGS achieves an
  average 70% reduction in model size over stateoftheart methods for dynamic 3D
  Gaussians from realworld dynamic videos while maintaining or even improving
  rendering quality.
paper_authors._id: 677e48854bf7f0d4735973d3
paper_authors.name: Sangwoon Kwak
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-08T13:51:17.553Z
paper_authors._id: 677e4ef9c27c05b2d178d775
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/xQoYxKiRmlnCOslOaxjCl.png
paper_authors.fullname: Sangwoon Kwak
paper_authors.type: user
paper_authors.user: sangwoonkwak
paper_authors._id: 677e48854bf7f0d4735973d4
paper_authors.name: Joonsoo Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-08T13:51:29.754Z
paper_authors._id: 660ca70399760c2ef3ab4450
paper_authors.avatarUrl: /avatars/d1dd8492b8129554b0650d22d0731fed.svg
paper_authors.fullname: Joonsoo Kim
paper_authors.type: user
paper_authors.user: joons1991
paper_authors._id: 677e48854bf7f0d4735973d5
paper_authors.name: Jun Young Jeong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-08T13:51:42.670Z
paper_authors._id: 677e68cc8a7269e1779e4def
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/PrMRHMrB4aEQPWV0pxNxX.png
paper_authors.fullname: Jun Young Jeong
paper_authors.type: user
paper_authors.user: shurek20
paper_authors._id: 677e48854bf7f0d4735973d6
paper_authors.name: Won-Sik Cheong
paper_authors._id: 677e48854bf7f0d4735973d7
paper_authors.name: Jihyong Oh
paper_authors._id: 677e48854bf7f0d4735973d8
paper_authors.name: Munchurl Kim
paper_title: MoDec-GS: Global-to-Local Motion Decomposition and Temporal Interval
    Adjustment for Compact Dynamic 3D Gaussian Splatting
paper_summary: 3D Gaussian Splatting (3DGS) has made significant strides in scene
  representation and neural rendering, with intense efforts focused on adapting
  it for dynamic scenes. Despite delivering remarkable rendering quality and
  speed, existing methods struggle with storage demands and representing complex
  real-world motions. To tackle these issues, we propose MoDecGS, a
  memory-efficient Gaussian splatting framework designed for reconstructing novel
  views in challenging scenarios with complex motions. We introduce GlobaltoLocal
  Motion Decomposition (GLMD) to effectively capture dynamic motions in a
  coarsetofine manner. This approach leverages Global Canonical Scaffolds (Global
  CS) and Local Canonical Scaffolds (Local CS), extending static Scaffold
  representation to dynamic video reconstruction. For Global CS, we propose
  Global Anchor Deformation (GAD) to efficiently represent global dynamics along
  complex motions, by directly deforming the implicit Scaffold attributes which
  are anchor position, offset, and local context features. Next, we finely adjust
  local motions via the Local Gaussian Deformation (LGD) of Local CS explicitly.
  Additionally, we introduce Temporal Interval Adjustment (TIA) to automatically
  control the temporal coverage of each Local CS during training, allowing
  MoDecGS to find optimal interval assignments based on the specified number of
  temporal segments. Extensive evaluations demonstrate that MoDecGS achieves an
  average 70% reduction in model size over stateoftheart methods for dynamic 3D
  Gaussians from realworld dynamic videos while maintaining or even improving
  rendering quality.
paper_ai_summary: MoDecGS is a memory-efficient Gaussian splatting framework for reconstructing challenging videos with complex motions, achieving significant model size reduction and maintaining rendering quality.
paper_ai_keywords: Gaussian splatting dynamic scenes rendering quality storage demands Global-to-Local Motion Decomposition GLMD Global Canonical Scaffolds Global CS Local Canonical Scaffolds Local CS Global Anchor Deformation GAD Local Gaussian Deformation LGD Temporal Interval Adjustment TIA