SimDEM: Audio-Driven Emotional Talking Head Generation With Simplified and Decoupled Expression Modeling

Emotional talking head generation has advanced significantly because of its potential to enhance naturalness and expressiveness in applications like gaming, virtual reality, and video conferencing. However, existing methods struggle with overcomplicated facial expression modeling, which introduces redundancies, reduces learning efficiency, and limits emotion accuracy. To address these limitations, we propose SimDEM, a novel framework for emotional talking head generation that leverages simplified and decoupled expression modeling. By retaining only critical dimensions of the expression representation via key expression modeling, we reduce noise in training and improve emotion accuracy. Furthermore, the decoupling of facial regions into independent dynamics for the upper and lower face enables more effective learning of localized emotional expressions, enhancing the visual fidelity of the output. Extensive evaluations demonstrate that SimDEM outperforms state-of-the-art methods, achieving superior emotion accuracy and visual quality in synthesizing expressive talking head videos.