Deformable Gaussian Splatting (GS) accomplishes photorealistic dynamic 3-D reconstruction from dense multi-view video (MVV) by learning to deform a canonical GS representation. However, in filmmaking, tight budgets can result in sparse camera configurations, which limits state-of-the-art (SotA) methods when capturing complex dynamic features. To address this issue, we introduce an approach that splits the canonical Gaussians and deformation field into foreground and background components using a sparse set of masks for frames at t=0. Each representation is separately trained on different loss functions during canonical pre-training. Then, during dynamic training, different parameters are modeled for each deformation field following common filmmaking practices. The foreground stage contains diverse dynamic features so changes in color, position and rotation are learned. While, the background containing film-crew and equipment, is typically dimmer and less dynamic so only changes in point position are learned. Experiments on 3-D and 2.5-D entertainment datasets show that our method produces SotA qualitative and quantitative results; up to 3 PSNR higher with half the model size on 3-D scenes. Unlike the SotA and without the need for dense mask supervision, our method also produces segmented dynamic reconstructions including transparent and dynamic textures. Code and video comparisons are available online: https://interims-git.github.io/

翻译：可变形高斯泼溅（GS）通过学习变形一个规范GS表示，实现了从密集多视角视频（MVV）中进行逼真的动态三维重建。然而在电影制作中，有限的预算可能导致稀疏的相机配置，这限制了现有最优（SotA）方法在捕捉复杂动态特征时的表现。为解决此问题，我们提出一种方法，利用t=0帧的稀疏掩码集合将规范高斯泼溅与变形场分解为前景和背景组件。在规范预训练阶段，每个表示通过不同的损失函数分别训练。随后在动态训练阶段，遵循电影制作的常见实践，为每个变形场建模不同的参数。前景阶段包含多样化的动态特征，因此学习颜色、位置和旋转的变化；而包含摄制组与设备的背景通常较暗且动态性较低，故仅学习点的位置变化。在三维与2.5维娱乐数据集上的实验表明，本方法在定性与定量结果上均达到SotA水平：在三维场景中，以一半的模型尺寸实现高达3 PSNR的提升。与SotA方法不同且无需密集掩码监督，本方法还能生成包含透明与动态纹理的分割动态重建结果。代码与视频对比已在线发布：https://interims-git.github.io/