Rendering photorealistic head avatars from arbitrary viewpoints is crucial for various applications like virtual reality. Although previous methods based on Neural Radiance Fields (NeRF) can achieve impressive results, they lack fidelity and efficiency. Recent methods using 3D Gaussian Splatting (3DGS) have improved rendering quality and real-time performance but still require significant storage overhead. In this paper, we introduce a method called GraphAvatar that utilizes Graph Neural Networks (GNN) to generate 3D Gaussians for the head avatar. Specifically, GraphAvatar trains a geometric GNN and an appearance GNN to generate the attributes of the 3D Gaussians from the tracked mesh. Therefore, our method can store the GNN models instead of the 3D Gaussians, significantly reducing the storage overhead to just 10MB. To reduce the impact of face-tracking errors, we also present a novel graph-guided optimization module to refine face-tracking parameters during training. Finally, we introduce a 3D-aware enhancer for post-processing to enhance the rendering quality. We conduct comprehensive experiments to demonstrate the advantages of GraphAvatar, surpassing existing methods in visual fidelity and storage consumption. The ablation study sheds light on the trade-offs between rendering quality and model size. The code will be released at: https://github.com/ucwxb/GraphAvatar

翻译：从任意视角渲染逼真的头部化身对于虚拟现实等应用至关重要。尽管先前基于神经辐射场（NeRF）的方法已能实现令人印象深刻的效果，但其在保真度与效率方面仍存在不足。近期采用三维高斯泼溅（3DGS）的方法虽提升了渲染质量与实时性能，却仍需较大的存储开销。本文提出一种名为GraphAvatar的方法，利用图神经网络（GNN）为头部化身生成三维高斯分布。具体而言，GraphAvatar通过训练几何GNN与外观GNN，从跟踪网格中生成三维高斯分布的属性参数。因此，本方法可仅存储GNN模型而非三维高斯数据，将存储开销显著降低至10MB。为降低面部跟踪误差的影响，我们进一步提出新颖的图引导优化模块，在训练过程中细化面部跟踪参数。最后，我们引入三维感知增强器进行后处理以提升渲染质量。通过全面实验验证了GraphAvatar的优越性，其在视觉保真度与存储消耗方面均超越现有方法。消融研究揭示了渲染质量与模型规模间的权衡关系。代码将发布于：https://github.com/ucwxb/GraphAvatar