SplitAvatar: One-shot Head Avatar with Autoregressive Gaussian Splitting

• URL: http://arxiv.org/abs/2605.25751v1
• Date: Mon, 25 May 2026 12:02:43 GMT
• Title: SplitAvatar: One-shot Head Avatar with Autoregressive Gaussian Splitting
• Abstract summary: We introduce a novel method for reconstructing an animatable head avatar from a single image.<n>We propose a Graph splitting network to progressively generate Gaussians from coarse to fine.<n>This process, enabled by the GNN-guided splitting, synthesizes more precise facial details and achieves higher reconstruction quality.
• Score: 26.626951365522526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) provides an efficient method for high-quality scene reconstruction using anisotropic Gaussians. Recently, 3DGS-based methods have significantly improved the rendering quality of human avatars while enabling real-time performance. However, existing methods suffer from a magnitude mismatch in the number of Gaussians generated by image-based and 3DMM-based approaches. This discrepancy results in reconstructed expressions that lack fine-grained detail. In this paper, we introduce a novel method for reconstructing an animatable head avatar from a single image. We propose a Graph splitting network to progressively generate Gaussians from coarse to fine using an autoregressive architecture. To address the graph inconsistency caused by split Gaussians, we employ a mesh topology extension method to align the GNN's connectivity with the increased Gaussian count. Furthermore, we introduce a novel density control method that includes a gating mechanism that generates soft masks for Gaussians, preventing over-densification after the splitting operation. This allows for dynamic control over Gaussian density across different facial regions. For smooth and rapid training, we employ a delayed filtering strategy to avoid re-computing the graph topology during training. Experimental results demonstrate that our autoregressive structure effectively improves expression representation ability by progressively splitting Gaussians. This process, enabled by the GNN-guided splitting, synthesizes more precise facial details and achieves higher reconstruction quality.

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