2023/12/05 by Helisa Dhamo, Yinyu Nie, Dhamo, Helisa +11 · 10 citations
Computer Science · #Face recognition and analysis #Human Pose and Action Recognition #Generative Adversarial Networks and Image Synthesis
paper · pdf · doi:10.48550/arxiv.2312.02902
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.