AI 中文总结
该研究针对虚拟人类表情生成难题，提出基于CVAEs的方法，利用含7680个样本的小数据集训练模型，可控制情绪强度，生成逼真表情，适用于虚拟角色动画。
AI 中文摘要
在虚拟人类（VHs）中生成富有表现力的面部行为，仍然是情感计算和角色动画领域的核心挑战。本文提出一种基于条件变分自编码器（CVAEs）的新方法，该方法在真实人类面部表情数据上进行训练，用于合成具有不同强度的可控情感表情。使用包含六种基本情绪（每种情绪分为低、高两个强度等级）的数据集，我们训练了一个CVAE模型来生成合成面部表情数据，同时保持与真实人类表情的语义一致性。尽管训练数据量有限（仅7680个面部表情样本），该方法仍能学习到有意义的潜在表示并生成连贯的情感变化。我们的方法可实现对情感强度的控制，无需演员表演或人工艺术干预，适用于虚拟角色动画。本研究旨在评估该CVAE方法是否保留数据集中不同强度等级的相关特征。结果表明，所提模型在各强度等级间保留了关键表达特征，同时支持跨情感强度等级的泛化能力，为从相对较小的数据集创建富有情感表现力的虚拟角色提供了支持。
英文摘要
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.
