  In recent years, the field of talking faces generation has attracted
considerable attention, with certain methods adept at generating virtual faces
that convincingly imitate human expressions. However, existing methods face
challenges related to limited generalization, particularly when dealing with
challenging identities. Furthermore, methods for editing expressions are often
confined to a singular emotion, failing to adapt to intricate emotions. To
overcome these challenges, this paper proposes EmoTalker, an emotionally
editable portraits animation approach based on the diffusion model. EmoTalker
modifies the denoising process to ensure preservation of the original
portrait's identity during inference. To enhance emotion comprehension from
text input, Emotion Intensity Block is introduced to analyze fine-grained
emotions and strengths derived from prompts. Additionally, a crafted dataset is
harnessed to enhance emotion comprehension within prompts. Experiments show the
effectiveness of EmoTalker in generating high-quality, emotionally customizable
facial expressions.
