Graph-Driven Micro-Expression Rendering with Emotionally Diverse Expressions for Lifelike Digital Humans.

ABSTRACT: Micro-expressions, characterized by brief and subtle facial muscle movements, are essential for conveying nuanced emotions in digital humans, yet existing rendering techniques often produce rigid or emotionally monotonous animations due to the inadequate modeling of temporal dynamics and action unit interdependencies. This paper proposes a graph-driven framework for micro-expression rendering that generates emotionally diverse and lifelike expressions. We employ a 3D-ResNet-18 backbone network to perform joint spatio-temporal feature extraction from facial video sequences, enhancing sensitivity to transient motion cues. Action units (AUs) are modeled as nodes in a symmetric graph, with edge weights derived from empirical co-occurrence probabilities and processed via a graph convolutional n

SUBMITTER: Fang L 

PROVIDER: S-EPMC12467307 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature