3D head animation has seen major quality and runtime improvements over the last few years, particularly empowered by the advances in differentiable rendering and neural radiance fields. Real-time rendering is a highly desirable goal for real-world applications. We propose HeadGaS, the first model to use 3D Gaussian Splats (3DGS) for 3D head reconstruction and animation. In this paper we introduce a hybrid model that extends the explicit representation from 3DGS with a base of learnable latent features, which can be linearly blended with low-dimensional parameters from parametric head models to obtain expression-dependent final color and opacity values. We demonstrate that HeadGaS delivers state-of-the-art results in real-time inference frame rates, which surpasses baselines by up to ~2dB, while accelerating rendering speed by over x10.
三维头部动画在过去几年里取得了重大的质量和运行时间改进，特别是受益于可微分渲染和神经辐射场的进步。实时渲染是现实世界应用中非常渴望达到的目标。我们提出了 HeadGaS，这是第一个使用三维高斯分散（3DGS）进行三维头部重建和动画的模型。在本文中，我们介绍了一种混合模型，该模型将来自3DGS的显式表示与可学习的潜在特征基底相结合，这些特征可以与参数头部模型中的低维参数线性混合，以获得表情依赖的最终颜色和不透明度值。我们展示了 HeadGaS 在实时推理帧率方面提供了最先进的结果，其性能超过基准线高达约2dB，同时加速渲染速度超过10倍。