ProgressiveAvatars: Progressive Animatable 3D Gaussian Avatars

Abstract

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.

Figures (7)

• Figure 1: Overview. We take head video as input then recover a tracked FLAME mesh sequence. We bind 3D Gaussians to the local coordinate frame of each FLAME face. During training, screen-space gradients of the Gaussians drive implicit subdivision of the template mesh across multiple levels, yielding a triangle face forest. At rendering time, we precompute per-face importance score and progressively transmit and render the corresponding Gaussians in decreasing order of importance.
• Figure 2: The center row shows the full model containing all 3D Gaussians within one level. Transmitting in descending importance makes early partial renderings closely match the full‑model pixel color because dominant contributors arrive first. In contrast, sending low‑importance Gaussians first re‑normalizes partial weights and amplifies weak contributors, causing noticeable color drift from the full model. This motivates an importance‑first schedule within each level for faithful progressive rendering.
• Figure 3: Qualitative results on NeRSemble dataset across different transmission percentages.
• Figure 4: Qualitative comparison with state-of-the-art methods.
• Figure 5: Comparison of adaptive and uniform subdivision. Right: visualization of per-face subdivision levels. High-frequency regions like facial hair receive more aggressive splitting, whereas smoother areas require substantially fewer subdivisions.