HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2312.02902v2
• Date: Tue, 13 Aug 2024 15:56:58 GMT
• Title: HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting
• Authors: Helisa Dhamo, Yinyu Nie, Arthur Moreau, Jifei Song, Richard Shaw, Yiren Zhou, Eduardo Pérez-Pellitero,
• Abstract summary: HeadGaS is a model that uses 3D Gaussian Splats (3DGS) for 3D head reconstruction and animation. We demonstrate that HeadGaS delivers state-of-the-art results in real-time inference frame rates, surpassing baselines by up to 2dB.
• Score: 11.849852156716171
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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