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arXiv · 2510.08587

EGSTalker: Real-Time Audio-Driven Talking Head Generation with Efficient Gaussian Deformation

Tianheng Zhu·Yinfeng Yu·Liejun Wang·Fuchun Sun·Wendong Zheng
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Abstract

This paper presents EGSTalker, a real-time audio-driven talking head generation framework based on 3D Gaussian Splatting (3DGS). Designed to enhance both speed and visual fidelity, EGSTalker requires only 3-5 minutes of training video to synthesize high-quality facial animations. The framework comprises two key stages: static Gaussian initialization and audio-driven deformation. In the first stage, a multi-resolution hash triplane and a Kolmogorov-Arnold Network (KAN) are used to extract spatial features and construct a compact 3D Gaussian representation. In the second stage, we propose an Efficient Spatial-Audio Attention (ESAA) module to fuse audio and spatial cues, while KAN predicts the corresponding Gaussian deformations. Extensive experiments demonstrate that EGSTalker achieves rendering quality and lip-sync accuracy comparable to state-of-the-art methods, while significantly outperforming them in inference speed. These results highlight EGSTalker's potential for real-time multimedia applications.

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Tianheng Zhu, Yinfeng Yu, Liejun Wang, Fuchun Sun, Wendong Zheng. 2025-10-03. EGSTalker: Real-Time Audio-Driven Talking Head Generation with Efficient Gaussian Deformation. https://arxiv.org/abs/2510.08587

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