In practical real-time XR and telepresence applications, network and computing resources fluctuate frequently. Therefore, a progressive 3D representation is needed. To this end, we propose ProgressiveAvatars, a progressive avatar representation built on a hierarchy of 3D Gaussians grown by adaptive implicit subdivision on a template mesh. 3D Gaussians are defined in face-local coordinates to remain animatable under varying expressions and head motion across multiple detail levels. The hierarchy expands when screen-space signals indicate a lack of detail, allocating resources to important areas. Leveraging importance ranking, ProgressiveAvatars supports incremental loading and rendering, adding new Gaussians as they arrive while preserving previous content, thus achieving smooth quality improvements across varying bandwidths. ProgressiveAvatars enables progressive delivery and progressive rendering under fluctuating network bandwidth and varying compute and memory resources.

翻译：在实际的实时扩展现实与远程呈现应用中，网络与计算资源频繁波动。因此，需要一种渐进式的三维表示方法。为此，我们提出了ProgressiveAvatars，一种基于三维高斯层次结构的渐进式虚拟化身表示方法，该层次结构通过在模板网格上进行自适应隐式细分而生成。三维高斯在面部局部坐标系中定义，以保持在多种细节级别下、不同表情与头部运动时的可动画性。当屏幕空间信号指示细节不足时，层次结构会进行扩展，将资源分配给重要区域。借助重要性排序机制，ProgressiveAvatars支持增量加载与渲染，在新高斯模型到达时添加它们，同时保留先前内容，从而在不同带宽下实现平滑的质量提升。ProgressiveAvatars能够在波动的网络带宽以及变化的计算与内存资源下，实现渐进式传输与渐进式渲染。