We propose selective-training Gaussian head avatars (STGA) to enhance the details of dynamic head Gaussian. The dynamic head Gaussian model is trained based on the FLAME parameterized model. Each Gaussian splat is embedded within the FLAME mesh to achieve mesh-based animation of the Gaussian model. Before training, our selection strategy calculates the 3D Gaussian splat to be optimized in each frame. The parameters of these 3D Gaussian splats are optimized in the training of each frame, while those of the other splats are frozen. This means that the splats participating in the optimization process differ in each frame, to improve the realism of fine details. Compared with network-based methods, our method achieves better results with shorter training time. Compared with mesh-based methods, our method produces more realistic details within the same training time. Additionally, the ablation experiment confirms that our method effectively enhances the quality of details.

翻译：我们提出选择性训练的高斯头部化身（STGA）以增强动态头部高斯模型的细节表现。该动态头部高斯模型基于FLAME参数化模型进行训练。每个高斯溅射点均嵌入FLAME网格中，从而实现高斯模型的网格驱动动画。在训练前，我们的选择策略会计算每帧中需要优化的三维高斯溅射点。这些三维高斯溅射点的参数在每帧训练中进行优化，而其余溅射点参数则保持冻结。这意味着参与优化过程的溅射点在不同帧中各不相同，从而提升精细细节的真实感。与基于神经网络的方法相比，本方法在更短的训练时间内取得了更优效果；与基于网格的方法相比，本方法在相同训练时间内能生成更真实的细节。消融实验进一步验证了本方法对细节质量的有效提升。