HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting
Helisa Dhamo Yinyu Nie Arthur Moreau Jifei Song Richard Shaw Yiren Zhou Eduardo Pérez-Pellitero
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.