Creating high-fidelity head avatars from multi-view videos is a core issue
for many AR/VR applications. However, existing methods usually struggle to
obtain high-quality renderings for all different head components simultaneously
since they use one single representation to model components with drastically
different characteristics (e.g., skin vs. hair). In this paper, we propose a
Hybrid Mesh-Gaussian Head Avatar (MeGA) that models different head components
with more suitable representations. Specifically, we select an enhanced FLAME
mesh as our facial representation and predict a UV displacement map to provide
per-vertex offsets for improved personalized geometric details. To achieve
photorealistic renderings, we obtain facial colors using deferred neural
rendering and disentangle neural textures into three meaningful parts. For hair
modeling, we first build a static canonical hair using 3D Gaussian Splatting. A
rigid transformation and an MLP-based deformation field are further applied to
handle complex dynamic expressions. Combined with our occlusion-aware blending,
MeGA generates higher-fidelity renderings for the whole head and naturally
supports more downstream tasks. Experiments on the NeRSemble dataset
demonstrate the effectiveness of our designs, outperforming previous
state-of-the-art methods and supporting various editing functionalities,
including hairstyle alteration and texture editing.

我们提出了一种混合 Mesh-Gaussian 头像的方法（MeGA），通过选择增强的 FLAME 网格作为面部表示，并使用延迟神经渲染获取面部颜色，从而为多个不同组件提供更适合的表示方式，以获得更高保真度的头部渲染，进而支持多种下游任务。