Cross-Style Character Expression Mapping and Intelligent Retrieval Based on Deep Learning

This paper presents a novel deep learning framework for cross-style character expression mapping and intelligent retrieval in animation production. Traditional methods often produce exaggerated or unnatural expressions when transferring between realistic faces and stylized characters due to fundamental differences in parameter constraints. We propose a comprehensive solution that combines convolutional neural networks with generative adversarial networks to create a robust cross-style mapping system. Our approach introduces a disentangled latent space representation that separates identity, expression, and style-specific components, coupled with an attention mechanism that focuses on emotionally significant facial regions during style transfer. The mapping network is guided by multiple loss functions that balance style consistency with expression preservation. The intelligent retrieval system leverages multi-modal feature embedding to organize expression libraries based on both emotional content and stylistic attributes, employing a context-aware ranking algorithm that considers production requirements. Experimental results demonstrate significant performance improvements over state-of-the-art methods, achieving 45.95dB PSNR and 0.936 SSIM in expression mapping quality, along with 0.91 precision@10 in retrieval accuracy. The proposed framework enables efficient cross-style expression asset reuse while maintaining emotional fidelity, addressing critical challenges in modern animation production pipelines.