We present personalized Gaussian Eigen Models (GEMs) for human heads, a novel method that compresses dynamic 3D Gaussians into low-dimensional linear spaces. Our approach is inspired by the seminal work of Blanz and Vetter, where a mesh-based 3D morphable model (3DMM) is constructed from registered meshes. Based on dynamic 3D Gaussians, we create a lower-dimensional representation of primitives that applies to most 3DGS head avatars. Specifically, we propose a universal method to distill the appearance of a mesh-controlled UNet Gaussian avatar using an ensemble of linear eigenbasis. We replace heavy CNN-based architectures with a single linear layer improving speed and enabling a range of real-time downstream applications. To create a particular facial expression, one simply needs to perform a dot product between the eigen coefficients and the distilled basis. This efficient method removes the requirement for an input mesh during testing, enhancing simplicity and speed in expression generation. This process is highly efficient and supports real-time rendering on everyday devices, leveraging the effectiveness of standard Gaussian Splatting. In addition, we demonstrate how the GEM can be controlled using a ResNet-based regression architecture. We show and compare self-reenactment and cross-person reenactment to state-of-the-art 3D avatar methods, demonstrating higher quality and better control. A real-time demo showcases the applicability of the GEM representation.

翻译：我们提出了个性化高斯特征模型（GEMs）用于人头建模，这是一种将动态三维高斯模型压缩到低维线性空间的新方法。我们的方法受到Blanz和Vetter开创性工作的启发，他们从配准的网格中构建了基于网格的三维可变形模型（3DMM）。基于动态三维高斯模型，我们创建了一个适用于大多数三维高斯溅射头像的基元低维表示。具体而言，我们提出了一种通用方法，通过线性特征基的集合来提取网格控制的UNet高斯头像的外观表征。我们用单一线性层取代了基于CNN的复杂架构，从而提升了速度并实现了一系列实时下游应用。要生成特定面部表情，只需在特征系数与提取的基之间进行点积运算。这种高效方法在测试阶段无需输入网格，增强了表情生成的简洁性与速度。该过程效率极高，借助标准高斯溅射的有效性，可在日常设备上实现实时渲染。此外，我们展示了如何通过基于ResNet的回归架构控制GEM模型。我们通过自驱动重演与跨人物重演实验，与前沿三维头像方法进行了对比验证，展现了更高的质量与更优的控制性能。实时演示进一步体现了GEM表征的实际应用潜力。