Construction of a multi-modal digital human education platform based on GAN and vision transformer

With the rapid development of artificial intelligence technology, digital human education platforms have become a research hotspot in education. This paper proposes a method to build a multi-modal digital human education platform based on a Generative Adversarial Network and a Vision Transformer. The platform enables high-quality avatar generation and interactive learning experiences. In the experimental part, we construct a large-scale dataset containing 1000 students and 50 teachers to evaluate the performance of the proposed method. The experimental results show that the proposed method has significantly improved avatars' authenticity, interaction response speed, and learning effect by comparing them with existing digital human education platforms. Specifically, the average recognition accuracy of avatars has increased by 12%, the interaction response time has been shortened by 25%, and students' academic performance has increased by 8% on average. This shows that the multi-modal digital human education platform based on GAN and ViT has excellent application potential and can provide new solutions for future education models.