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  1. Home
  3. Graph-driven Micro-expression Rendering With Emotionally Diverse Expressions For Lifelike Digital Humans.
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  3. Graph-driven Micro-expression Rendering With Emotionally Diverse Expressions For Lifelike Digital Humans.

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

Lei Fang1, Fan Yang1, Yichen Lin2

  • 1Department of Emotion Engineering, Sangmyung University, Seoul 03016, Republic of Korea.

Biomimetics (Basel, Switzerland)
|September 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a graph-driven framework for rendering realistic micro-expressions in digital humans. The new method improves emotional diversity and animation naturalness by modeling facial action unit dependencies.

Keywords:
FACIAL Action Coding System (FACS)action unitsdigital humansgraph convolutional network (GCN)micro-expressionsspatiotemporal feature extraction

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Area of Science:

  • Computer Graphics and Animation
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Micro-expressions are vital for conveying nuanced emotions in digital humans.
  • Existing rendering techniques struggle with temporal dynamics and action unit interdependencies, leading to rigid animations.
  • There is a need for advanced methods to generate lifelike and emotionally diverse micro-expressions.

Purpose of the Study:

  • To propose a novel graph-driven framework for micro-expression rendering.
  • To enhance the emotional diversity and realism of digital human facial animations.
  • To bridge the gap between micro-expression recognition and high-fidelity facial animation.

Main Methods:

  • Utilized a 3D-ResNet-18 backbone for joint spatio-temporal feature extraction from facial videos.
  • Modeled Action Units (AUs) as nodes in a symmetric graph, capturing dependencies with graph convolutional networks.
  • Employed B-spline functions for interpolating AU activations into continuous motion curves for real-time animation (Unreal Engine).

    • Main Results:

    • Achieved superior performance on the CASME II dataset with an F1-score of 77.93% and accuracy of 84.80% (5-fold cross-validation).
    • Demonstrated improved temporal segmentation compared to existing baseline methods.
    • Subjective evaluations confirmed enhanced perceptual clarity, naturalness, and realism in rendered digital humans.

    Conclusions:

    • The proposed graph-driven framework effectively renders emotionally diverse and lifelike micro-expressions.
    • Modeling AU interdependencies using symmetric graphs significantly improves animation quality.
    • This approach enables more expressive and realistic virtual interactions in digital human applications.