MoDec-GS: Global-to-Local Motion Decomposition and Temporal Interval
  Adjustment for Compact Dynamic 3D Gaussian Splatting
Authors: name: Sangwoon Kwak; name: Joonsoo Kim; name: Jun Young Jeong; name: Won-Sik Cheong; name: Jihyong Oh; name: Munchurl Kim
Published: 2025-01-07 11:43:13

Abstract: 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.