Dynamic extensions of 3D Gaussian Splatting (3DGS) achieve high-quality reconstructions through neural motion fields, but per-Gaussian neural inference makes these models computationally expensive. Building on DeformableGS, we introduce Speedy Deformable 3D Gaussian Splatting (SpeeDe3DGS), which bridges this efficiency-fidelity gap through three complementary modules: Temporal Sensitivity Pruning (TSP) removes low-impact Gaussians via temporally aggregated sensitivity analysis, Temporal Sensitivity Sampling (TSS) perturbs timestamps to suppress floaters and improve temporal coherence, and GroupFlow distills the learned deformation field into shared SE(3) transformations for efficient groupwise motion. On the 50 dynamic scenes in MonoDyGauBench, integrating TSP and TSS into DeformableGS accelerates rendering by 6.78$\times$ on average while maintaining neural-field fidelity and using 10$\times$ fewer primitives. Adding GroupFlow culminates in 13.71$\times$ faster rendering and 2.53$\times$ shorter training, surpassing all baselines in speed while preserving superior image quality.

翻译：三维高斯泼溅（3DGS）的动态扩展通过神经运动场实现了高质量重建，但每个高斯元的神经推理使得这些模型计算成本高昂。基于DeformableGS，我们提出快速可变形三维高斯泼溅（SpeeDe3DGS），通过三个互补模块弥合了效率与保真度之间的差距：时间敏感性剪枝（TSP）通过时间聚合的敏感性分析移除低影响高斯元；时间敏感性采样（TSS）通过扰动时间戳抑制浮点并增强时间连贯性；GroupFlow则将学习到的变形场蒸馏为共享的SE(3)变换，以实现高效的群组运动。在MonoDyGauBench的50个动态场景上，将TSP和TSS集成到DeformableGS中，平均渲染速度提升6.78倍，同时保持神经场保真度且减少10倍基元数量。进一步加入GroupFlow后，渲染速度提升13.71倍，训练时间缩短2.53倍，在保持卓越图像质量的同时超越了所有基线方法的速度表现。