While Dynamic Gaussian Splatting enables high-fidelity 4D reconstruction, its deployment is severely hindered by a fundamental dilemma: unconstrained densification leads to excessive memory consumption incompatible with edge devices, whereas heuristic pruning fails to achieve optimal rendering quality under preset Gaussian budgets. In this work, we propose Constrained Dynamic Gaussian Splatting (CDGS), a novel framework that formulates dynamic scene reconstruction as a budget-constrained optimization problem to enforce a strict, user-defined Gaussian budget during training. Our key insight is to introduce a differentiable budget controller as the core optimization driver. Guided by a multi-modal unified importance score, this controller fuses geometric, motion, and perceptual cues for precise capacity regulation. To maximize the utility of this fixed budget, we further decouple the optimization of static and dynamic elements, employing an adaptive allocation mechanism that dynamically distributes capacity based on motion complexity. Furthermore, we implement a three-phase training strategy to seamlessly integrate these constraints, ensuring precise adherence to the target count. Coupled with a dual-mode hybrid compression scheme, CDGS not only strictly adheres to hardware constraints (error < 2%}) but also pushes the Pareto frontier of rate-distortion performance. Extensive experiments demonstrate that CDGS delivers optimal rendering quality under varying capacity limits, achieving over 3x compression compared to state-of-the-art methods.

翻译：尽管动态高斯泼溅技术能够实现高保真度的四维重建，但其实际部署受到一个根本性困境的严重阻碍：无约束的致密化会导致过高的内存消耗，无法在边缘设备上运行；而启发式剪枝方法在预设的高斯预算下又无法达到最优的渲染质量。本文提出约束动态高斯泼溅（CDGS），这是一个新颖的框架，它将动态场景重建表述为一个预算约束的优化问题，从而在训练期间强制执行严格、用户定义的高斯预算。我们的核心洞见是引入一个可微分的预算控制器作为核心优化驱动。该控制器在几何、运动和感知线索融合而成的多模态统一重要性分数指导下，实现精确的容量调控。为了最大化这一固定预算的效用，我们进一步解耦了静态与动态元素的优化，采用一种自适应分配机制，根据运动复杂度动态分配容量。此外，我们实现了一个三阶段训练策略，以无缝集成这些约束，确保精确遵循目标数量。结合双模式混合压缩方案，CDGS不仅严格遵循硬件约束（误差 < 2%），而且推进了率失真性能的帕累托前沿。大量实验表明，CDGS在不同容量限制下均能提供最优的渲染质量，与最先进方法相比实现了超过3倍的压缩比。