MoDec-GS: Global-to-Local Motion Decomposition and Temporal Interval
  Adjustment for Compact Dynamic 3D Gaussian Splatting
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.
677e48854bf7f0d4735973d3
Sangwoon Kwak
admin_assigned
2025-01-08T13:51:17.553Z
677e4ef9c27c05b2d178d775
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/xQoYxKiRmlnCOslOaxjCl.png
Sangwoon Kwak
user
sangwoonkwak
677e48854bf7f0d4735973d4
Joonsoo Kim
admin_assigned
2025-01-08T13:51:29.754Z
660ca70399760c2ef3ab4450
/avatars/d1dd8492b8129554b0650d22d0731fed.svg
Joonsoo Kim
user
joons1991
677e48854bf7f0d4735973d5
Jun Young Jeong
admin_assigned
2025-01-08T13:51:42.670Z
677e68cc8a7269e1779e4def
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/PrMRHMrB4aEQPWV0pxNxX.png
Jun Young Jeong
user
shurek20
677e48854bf7f0d4735973d6
Won-Sik Cheong
677e48854bf7f0d4735973d7
Jihyong Oh
677e48854bf7f0d4735973d8
Munchurl Kim
MoDec-GS: Global-to-Local Motion Decomposition and Temporal Interval
  Adjustment for Compact Dynamic 3D Gaussian Splatting
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.
MoDecGS is a memory-efficient Gaussian splatting framework for reconstructing challenging videos with complex motions, achieving significant model size reduction and maintaining rendering quality.
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