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, a model that uses 3D Gaussian Splats (3DGS) for 3D head reconstruction and animation. In this paper we introduce a hybrid model that extends the explicit 3DGS representation with a base of learnable latent features, which can be linearly blended with low-dimensional parameters from parametric head models to obtain expression-dependent color and opacity values. We demonstrate that HeadGaS delivers state-of-the-art results in real-time inference frame rates, surpassing baselines by up to 2dB, while accelerating rendering speed by over x10.
@article{arxiv.2312.02902,
title = {HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting},
author = {Helisa Dhamo and Yinyu Nie and Arthur Moreau and Jifei Song and Richard Shaw and Yiren Zhou and Eduardo Pérez-Pellitero},
journal= {arXiv preprint arXiv:2312.02902},
year = {2024}
}