ESGaussianFace: Emotional and Stylized Audio-Driven Facial Animation via 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2601.01847v1
• Date: Mon, 05 Jan 2026 07:19:38 GMT
• Title: ESGaussianFace: Emotional and Stylized Audio-Driven Facial Animation via 3D Gaussian Splatting
• Authors: Chuhang Ma, Shuai Tan, Ye Pan, Jiaolong Yang, Xin Tong,
• Abstract summary: ESGaussianFace is an innovative framework for emotional and stylized audio-driven facial animation.<n>We propose an emotion-audio-guided spatial attention method that effectively integrates emotion features with audio content features.<n>Our generated results exhibit high efficiency, high quality, and 3D consistency.
• Score: 34.65130896150361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most current audio-driven facial animation research primarily focuses on generating videos with neutral emotions. While some studies have addressed the generation of facial videos driven by emotional audio, efficiently generating high-quality talking head videos that integrate both emotional expressions and style features remains a significant challenge. In this paper, we propose ESGaussianFace, an innovative framework for emotional and stylized audio-driven facial animation. Our approach leverages 3D Gaussian Splatting to reconstruct 3D scenes and render videos, ensuring efficient generation of 3D consistent results. We propose an emotion-audio-guided spatial attention method that effectively integrates emotion features with audio content features. Through emotion-guided attention, the model is able to reconstruct facial details across different emotional states more accurately. To achieve emotional and stylized deformations of the 3D Gaussian points through emotion and style features, we introduce two 3D Gaussian deformation predictors. Futhermore, we propose a multi-stage training strategy, enabling the step-by-step learning of the character's lip movements, emotional variations, and style features. Our generated results exhibit high efficiency, high quality, and 3D consistency. Extensive experimental results demonstrate that our method outperforms existing state-of-the-art techniques in terms of lip movement accuracy, expression variation, and style feature expressiveness.

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