【摘要】 文章主要探讨了提升虚拟数字人表情自然度的方法,深入研究了深度学习算法在该领域的应用效果。通过改进生成对抗网络（GAN）和卷积神经网络（CNN）,提升了表情生成的质量。同时,采用多模态数据融合技术,把不同种类的数据整合起来,结合情感驱动的优化策略,使生成的表情看起来更自然流畅、更有真实感。本文研究为虚拟数字人表情生成带来了新思路,在一定程度上促进了人机交互技术的进步。更多还原

【Abstract】 The article mainly explores methods to improve the naturalness of virtual digital human expressions and deeply studies the application effect of deep learning algorithms in this field.By improving the Generative Adversarial Network(GAN) and Convolutional Neural Network(CNN),the quality of facial expression generation has been enhanced.At the same time,multimodal data fusion technology is adopted to integrate different types of data,combined with emotion driven optimization strategies,to make the generated expressions look more natural,smooth,and realistic.This study provides new ideas for the generation of virtual digital human expressions,which to some extent promotes the progress of human-computer interaction technology.更多还原