EmoDiffTalk:Emotion-aware Diffusion for Editable 3D Gaussian Talking Head

• URL: http://arxiv.org/abs/2512.05991v1
• Date: Sun, 30 Nov 2025 16:28:19 GMT
• Title: EmoDiffTalk:Emotion-aware Diffusion for Editable 3D Gaussian Talking Head
• Authors: Chang Liu, Tianjiao Jing, Chengcheng Ma, Xuanqi Zhou, Zhengxuan Lian, Qin Jin, Hongliang Yuan, Shi-Sheng Huang,
• Abstract summary: This paper introduces a new editable 3D Gaussian talking head, i.e. EmoDiffTalk.<n>Our key idea is a novel Emotion-aware Gaussian Diffusion.<n>EmoDiffTalk is one of the first few 3D Gaussian Splatting talking-head generation framework.
• Score: 42.33255633480444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent photo-realistic 3D talking head via 3D Gaussian Splatting still has significant shortcoming in emotional expression manipulation, especially for fine-grained and expansive dynamics emotional editing using multi-modal control. This paper introduces a new editable 3D Gaussian talking head, i.e. EmoDiffTalk. Our key idea is a novel Emotion-aware Gaussian Diffusion, which includes an action unit (AU) prompt Gaussian diffusion process for fine-grained facial animator, and moreover an accurate text-to-AU emotion controller to provide accurate and expansive dynamic emotional editing using text input. Experiments on public EmoTalk3D and RenderMe-360 datasets demonstrate superior emotional subtlety, lip-sync fidelity, and controllability of our EmoDiffTalk over previous works, establishing a principled pathway toward high-quality, diffusion-driven, multimodal editable 3D talking-head synthesis. To our best knowledge, our EmoDiffTalk is one of the first few 3D Gaussian Splatting talking-head generation framework, especially supporting continuous, multimodal emotional editing within the AU-based expression space.

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