动态分层3D高斯人像，自适应加载细节，适配网络波动。
ProgressiveAvatars: Progressive Animatable 3D Gaussian Avatars
- 基于模板网格的自适应细分，构建可动画的分层3D高斯表示
- 屏幕空间信号触发细节扩展，关键区域优先分配资源
- 支持渐进式加载渲染，适合实时远程交互场景
在实际的实时扩展现实（XR）与远程存在应用中，网络与计算资源常发生波动。为此，我们提出ProgressiveAvatars，一种基于分层3D高斯的渐进式虚拟人表示方法，其通过模板网格上的自适应隐式细分逐步生长。3D高斯在面局部坐标系中定义，确保在不同表情和头部运动下仍保持可动画性，并支持多级细节。当屏幕空间信号显示细节不足时，层级会扩展，将资源分配至重要区域。借助重要性排序，ProgressiveAvatars支持增量式加载与渲染，在新高斯到达时逐步添加，同时保留已有内容，实现带宽波动下的平滑质量提升。该方法可在资源变化条件下实现渐进式交付与渲染。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。