PESTalk: Speech-Driven 3D Facial Animation with Personalized Emotional Styles

摘要

Speech-driven 3D facial animation aims to synthesize realistic emotional facial expressions that match the input speech. However, existing approaches are constrained by two key limitations: (1) These methods rely on pre-trained models (e.g., Wav2Vec 2.0) as audio emotion feature extractors, which neglect critical frequency-domain characteristics, thereby emphasizing the challenge of discriminating between similar emotion categories. (2) They treat audio emotions as generic categorical states, ignoring individual differences in emotional expression, ultimately producing over-smoothed emotional representations that appear repetitive and stereotypical. To that end, we introduce PESTalk, a novel approach that generates 3D facial animations with Personalized Emotional Styles directly from speech inputs, thus significantly enhancing the realism of facial animations. Specifically, since acoustic frequency cues contain essential emotional information, we first propose a Dual-Stream Emotion Extractor (DSEE ), which captures both time-domain variations and frequency-domain characteristics of audio signals to extract fine-grained affective features and subtle emotional nuances. Furthermore, we design an Emotional Style Modeling Module (ESMM ) to achieve personalized emotional styles. This module first establishes a baseline representation for each subject based on voiceprint characteristics, then progressively refines it by continuously integrating emotional features. Ultimately, this process constructs a personalized emotional style representation for each subject in each emotion category, capturing their unique expression patterns. Finally, considering the scarcity of the 3D emotional talking face data, we employ an advanced facial capture model to extract pseudo facial blendshape coefficients from 2D emotional data, thereby constructing a large-scale 3D emotional talking face dataset with diverse emotions and personalized expressions (3D-EmoStyle). Extensive quantitative and qualitative evaluations show that PESTalk can generate realistic 3D facial animation and outperform state-of-the-art methods. The codes and dataset are available at: https://github.com/tianshunhan/PESTalk.