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.
我们提出了用于人类头部的个性化高斯特征模型（Gaussian Eigen Models，GEMs），这是一种将动态3D高斯函数压缩为低维线性空间的新方法。我们的方法受到Blanz和Vetter的开创性工作的启发，他们构建了基于网格的3D可变形模型（3DMM），从注册的网格中得出。基于动态3D高斯函数，我们创建了一种适用于大多数3D头部虚拟形象的低维表示方法。具体而言，我们提出了一种通用方法，通过一组线性特征基，精炼控制网格的UNet高斯化身形象。我们用单一线性层替换了复杂的基于CNN的架构，提高了速度，并使一系列实时下游应用成为可能。要创建特定的面部表情，只需对特征系数和精炼基之间进行点积运算。这种高效的方法在测试期间消除了对输入网格的需求，增强了生成表情的简易性和速度。这一过程非常高效，并支持在日常设备上实时渲染，充分利用了标准高斯光滑的有效性。此外，我们展示了如何使用基于ResNet的回归架构来控制GEM。我们展示并比较了自我重现和跨人重现与最先进的3D虚拟形象方法，显示了更高的质量和更好的控制。一个实时演示展示了GEM表示的适用性。