Dynamic Gaussian compression is normally optimized for complete files or complete progressive prefixes, but interactive rendering encounters partial representations: some spatiotemporal regions are present, others missing, and late refinements cannot affect the displayed frame. We study dynamic Gaussian representations whose incomplete delivery states remain directly renderable and whose degradation is optimized in image space. Gaussian primitives are organized into independently addressable spatiotemporal clusters with a base level and three refinements; training samples partial dependency graphs, renders many counterfactual states in one GPU batch, and minimizes expected distortion, tail distortion, temporal inconsistency, rate, and prefix regressions. A counterfactual utility layer measures the marginal render contribution of each completion group across valid receiver contexts. The same graph admits a concrete delivery realization with MTU-bounded entropy-coded chunks, deadline-aware scheduling, and receiver-side dependency closure. On held-out views, the finest refinement has negative mean marginal utility in 3/32 D-NeRF bouncingballs, 49/64 HyperNeRF broom2, and 28/64 HyperNeRF chicken clusters; its lower-tail utility is negative in 21/32, 61/64, and 42/64 clusters, respectively. On broom2, render-utility ordering removes both PSNR regressions produced by nominal layer order at matched byte budgets; on chicken, utilities measured on disjoint training cameras improve held-out PSNR by 3.03 dB at the lowest matched budget. These scoped results show why nominal refinement order cannot substitute for render-conditioned utility: the formulation treats network delivery as a distribution over renderable scene states rather than as an external wrapper around a graphics codec.

翻译：动态高斯压缩通常针对完整文件或完整渐进式前缀进行优化，但交互式渲染会遭遇部分表示：部分时空区域存在而其他缺失，且后期精细化无法影响已显示的帧。我们研究动态高斯表示，其不完全交付状态仍可直接渲染，并且其退化在图像空间中得到优化。高斯基元被组织为可独立寻址的时空簇，包含一个基础层级和三个精细化层级；训练样本部分依赖图，在单个GPU批次中渲染多个反事实状态，并最小化预期失真、尾部失真、时间不一致性、码率和前缀退化。反事实效用层在有效接收者上下文中衡量每个完成组的边际渲染贡献。同一依赖图可实现具体的交付方案，包括MTU界限熵编码块、截止时间感知调度以及接收端依赖闭包。在保留视图上，最精细的精细化在3/32个D-NeRF弹跳球、49/64个HyperNeRF扫帚2和28/64个HyperNeRF鸡群中具有负平均边际效用；其下尾效用分别在这些簇中的21/32、61/64和42/64个中为负。在扫帚2上，渲染效用排序消除了标称层级顺序在匹配字节预算下产生的所有PSNR退化；在鸡群上，在不相交训练相机上测量的效用将最低匹配预算下的保留PSNR提高了3.03 dB。这些限定结果表明为何标称精细化顺序无法替代渲染条件效用：该公式将网络交付视为可渲染场景状态的分布，而非图形编解码器的外部包装。