Cross-Modal Emotion Transfer for Emotion Editing in Talking Face Video

ArXi:2604.07786v1 Announce Type: new Talking face generation has gained significant attention as a core application of generative models. To enhance the expressiveness and realism of synthesized videos, emotion editing in talking face video plays a crucial role. However, existing approaches often limit expressive flexibility and struggle to generate extended emotions. Label-based methods represent emotions with discrete categories, which fail to capture a wide range of emotions.