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.
3D 高斯点绘制（3D Gaussian Splatting, 3DGS）在场景表示和神经渲染领域取得了显著进展，尤其在动态场景中的适配上备受关注。尽管现有方法在渲染质量和速度上表现出色，但它们在存储需求和复杂真实场景的动态运动表示方面仍存在挑战。为了解决这些问题，我们提出了 MoDecGS，一种内存高效的高斯点绘制框架，旨在应对复杂运动场景中的新视角重建。 我们引入了 全局到局部运动分解（Global-to-Local Motion Decomposition, GLMD），以粗到细的方式高效捕捉动态运动。该方法利用 全局规范支架（Global Canonical Scaffold, Global CS） 和 局部规范支架（Local Canonical Scaffold, Local CS），将静态支架表示扩展到动态视频重建。对于 Global CS，我们提出了 全局锚点变形（Global Anchor Deformation, GAD），通过直接变形锚点位置、偏移量和局部上下文特征等隐式支架属性，高效表示复杂运动中的全局动态。随后，通过对 Local CS 的 局部高斯变形（Local Gaussian Deformation, LGD），显式调整局部运动。 此外，我们引入了 时间间隔调整（Temporal Interval Adjustment, TIA），在训练过程中自动控制每个 Local CS 的时间覆盖范围，使 MoDecGS 能够基于指定的时间段数找到最优的时间间隔分配。 大量实验表明，MoDecGS 在处理真实动态视频的动态 3D 高斯场景时，相较于最先进方法，模型尺寸平均减少了 70%，同时在渲染质量上保持甚至有所提升。