Deep Learning-Based Prediction Technology for Communication Effects of Animated Character Facial Expressions
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Abstract
The increasing demand for engaging animated content requires advanced mechanisms to predict communication effectiveness prior to production completion. This study introduces a deep learning framework designed to forecast audience engagement through automated analysis of animated characters' facial expressions. The methodology combines convolutional neural networks for precise facial feature extraction with transformer architectures for temporal prediction of communication impact. By processing visual expression data and correlating it with audience response patterns, the framework achieves 87.3% accuracy in predicting viral potential across a range of animation styles. Compared to traditional content evaluation methods, this approach significantly reduces production iteration cycles by 42% while preserving creative authenticity. Experimental validation using 15,000 animated sequences from commercial productions confirms the framework's effectiveness in predicting audience emotional resonance and content shareability across diverse demographic groups.
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