Emotion Intensity Matters: Generating Realistic Expressions in Virtual Humans with CVAEs

2026-08-21

AI Summary

该研究使用CVAEs方法生成具有不同强度的真实感面部表情，解决了虚拟人物表情生成的挑战，且在少量数据下保持了情感表达的一致性和可控性。

Abstract

Generating expressive facial behavior in virtual humans (VHs) remains a central challenge in affective computing and character animation. This paper presents a novel approach based on Conditional Variational Autoencoders (CVAEs), trained on real human facial expression data, to synthesize controllable emotional expressions at varying intensities. Using a dataset comprising six basic emotions represented at two intensity levels (low and high), we train a CVAE model to generate synthetic facial expression data while preserving semantic consistency with real human expressions. Despite the limited amount of training data (only 7,680 facial expression samples), the proposed approach learns meaningful latent representations and generates coherent emotional variations. Our method enables control over emotional intensity, making it suitable for animating virtual characters without requiring actor performances or manual artistic intervention. Our research aimed to evaluate whether the method (CVAE) preserves the characteristics associated with the different intensity levels present in the dataset. Results show that the proposed model preserves key expressive characteristics across intensity levels while supporting generalization across emotional intensity levels, contributing to the creation of emotionally expressive virtual characters from relatively small datasets.

Problem

• Emotion Intensity
• Virtual Humans
• Facial Expressions
• CVAEs

Innovation

• Conditional Variational Autoencoders
• Emotional Intensity Control
• Facial Expression Synthesis