Over the past years, we have observed an abundance of approaches for modeling dynamic 3D scenes using Gaussian Splatting (GS). Such solutions use GS to represent the scene's structure and the neural network to model dynamics. Such approaches allow fast rendering and extracting each element of such a dynamic scene. However, modifying such objects over time is challenging. SC-GS (Sparse Controlled Gaussian Splatting) enhanced with Deformed Control Points partially solves this issue. However, this approach necessitates selecting elements that need to be kept fixed, as well as centroids that should be adjusted throughout editing. Moreover, this task poses additional difficulties regarding the re-productivity of such editing. To address this, we propose Dynamic Multi-Gaussian Soup (D-MiSo), which allows us to model the mesh-inspired representation of dynamic GS. Additionally, we propose a strategy of linking parameterized Gaussian splats, forming a Triangle Soup with the estimated mesh. Consequently, we can separately construct new trajectories for the 3D objects composing the scene. Thus, we can make the scene's dynamic editable over time or while maintaining partial dynamics.

翻译：近年来，我们观察到大量利用高斯泼溅（Gaussian Splatting, GS）对动态三维场景进行建模的方法。此类方案使用GS表示场景结构，并借助神经网络建模动态特性。这些方法能够实现快速渲染并提取动态场景中的各个元素。然而，随时间推移修改此类对象仍具挑战性。通过变形控制点增强的SC-GS（稀疏控制高斯泼溅）部分解决了该问题。但该方法需要选择需保持固定的元素，以及在整个编辑过程中需调整的质心。此外，此类编辑的可复现性也带来了额外困难。为此，我们提出动态多高斯汤（Dynamic Multi-Gaussian Soup, D-MiSo），能够对动态GS进行网格启发的表征建模。同时，我们提出一种参数化高斯泼溅的链接策略，与估计的网格共同构成三角汤（Triangle Soup）。由此，我们可以为构成场景的三维对象分别构建新的运动轨迹。这使得场景动态能够随时间进行编辑，或在保持部分动态特性的同时进行调整。