EmoTalk3D: High-Fidelity Free-View Synthesis of Emotional 3D Talking Head

Qianyun He, Xinya Ji, Yicheng Gong, Yuanxun Lu, Zhengyu Diao, Linjia Huang, Yao Yao, Siyu Zhu, Zhan Ma, Songcen Xu, Xiaofei Wu, Zixiao Zhang, Xun Cao, Hao Zhu* ;

"We present a novel approach for synthesizing 3D talking heads with controllable emotion, featuring enhanced lip synchronization and rendering quality. Despite significant progress in the field, prior methods still suffer from multi-view consistency and a lack of emotional expressiveness. To address these issues, we collect dataset with calibrated multi-view videos, emotional annotations, and per-frame 3D geometry. By training on the dataset, we propose a ‘Speech-to-Geometry-to-Appearance’ mapping framework that first predicts faithful 3D geometry sequence from the audio features, then the appearance of a 3D talking head represented by 4D Gaussians is synthesized from the predicted geometry. The appearance is further disentangled into canonical and dynamic Gaussians, learned from multi-view videos, and fused to render free-view talking head animation. Moreover, our model enables controllable emotion in the generated talking heads and can be rendered in wide-range views. Our method exhibits improved rendering quality and stability in lip motion generation while capturing dynamic facial details such as wrinkles and subtle expressions. Experiments demonstrate the effectiveness of our approach in generating high-fidelity and emotion-controllable 3D talking heads. The code and dataset are released in https://nju-3dv.github.io/ projects/EmoTalk3D."