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

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 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 for joint spatiotemporal feature extraction from facial video sequences, enhancing sensitivity to transient motion cues. Action units are modeled as nodes in an asymmetric graph, with edge weights derived from empirical co-occurrence probabilities, and processed through a graph convolutional network to capture structural dependencies and asymmetric interactions. Predicted action unit activations are interpolated into continuous motion curves using B-spline functions, modulated by emotion-specific intensity parameters, and mapped to skeletal controls in a real-time animation pipeline (Unreal Engine). Experiments on the CASME II dataset demonstrate superior performance, achieving an F1-score of 0.623 and accuracy of 64.1%, outperforming baselines in temporal segmentation and expression diversity. Subjective evaluations confirm enhanced perceptual clarity, naturalness, and authenticity in rendered digital humans. This approach bridges micro-expression recognition and high-fidelity facial animation, enabling more expressive virtual interactions.