Enhancing Cross-Cultural Communication Through Innovations in Digital Humans and Generative Art

Vollständiger Abstract

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This study investigates how digital humans and generative art can enhance inclusive, culturally informed cross-cultural communication. It first notes limitations of traditional AI models—such as cultural bias from skewed training data, poor localization, and rigid feedback mechanisms—that lead to misinterpretations or inauthentic cultural representation. An innovative model integrating multimodal context recognition (capturing language, facial expressions, tone) and dynamic feedback optimization is then proposed, with a multi-loop structure enabling real-time adjustments to cultural cues. Validated across five languages using datasets like MuST-C and real-world case studies, the model outperforms traditional counterparts: it achieves a 90 ms response time, high cultural adaptability, and up to 93.7% user satisfaction. Findings confirm the model's value in facilitating global cultural knowledge transfer and support its potential for educational, heritage, and international collaboration scenarios.