EmoDiffTalk: Emotion-aware Diffusion for Editable 3D Gaussian Talking Head
Chang Liu, Tianjiao Jing, Chengcheng Ma, Xuanqi Zhou, Zhengxuan Lian, Qin Jin, Hongliang Yuan, Shi-Sheng Huang
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.Please visit our website for more details and information:https://liuchang883.github.io/EmoDiffTalk/
BibTeX
@inproceedings{cvpr2026_emodifftalkemoti,
title = {EmoDiffTalk: Emotion-aware Diffusion for Editable 3D Gaussian Talking Head},
author = {Chang Liu and Tianjiao Jing and Chengcheng Ma and Xuanqi Zhou and Zhengxuan Lian and Qin Jin and Hongliang Yuan and Shi-Sheng Huang},
booktitle = {CVPR 2026},
year = {2026}
}