Enhancing Emotional Realism in Games: An Optimized Generative AI Framework for Dynamic 3D Facial Animation

Facial expressiveness is essential for immersive gaming, yet generating emotionally responsive 3D characters remains challenging. This paper presents an optimized generative AI framework that integrates OpenAI's LLMs with OpenFace to improve the mapping between blendshapes (or morph targets) and facial action units (standardized facial muscle movements defined by Facial Action Coding System) for dynamic facial expressions. The system, embedded in an original game scene, generates facial expressions based on interactive player dialogues. From a software engineering perspective, it features modular, scalable AI-driven animation pipelines that support adaptive emotion modeling and game engine integration. User evaluations demonstrate high realism, clear emotion recognition, and strong engagement, confirming the system's potential to enhance player interaction. Further refinements in blendshape mappings and real-time adjustments can enhance the differentiation of subtle emotions like Disgust and Contempt, improving the system's overall expressiveness.