DisenEmo: Learning disentangled emotional representation from facial motion for 3D talking head generation

Emotional 3D talking head generation synthesizes vivid facial expressions with precise lip synchronization for immersive interactions. We introduce DisenEMO, a novel framework designed to disentangle emotion and content from facial motions, thereby facilitating the synthesis of personalized and expressive audio-driven facial animations. To achieve precise emotional disentanglement, we incorporate an intensity perception constraint which improves the accurate perception of categorized emotion and its intensities, leading to the generation of subtle emotional expressions. To ensure the temporal consistency of facial expressions, we introduce facial dynamic modeling, which refines motion trajectories to better capture emotional nuances. Finally, a motion decoder integrates emotional features with audio features extracted from driving speech, producing 3D talking heads with enhanced emotional expressiveness and realism. Experimental results demonstrate that our method outperforms state-of-the-art approaches. Synthesis samples are available at https://c295bw.github.io/DisenEMO-icip25.github.io/.