Existing 3D facial emotion modeling have been constrained by limited emotion classes and insufficient datasets. This paper introduces "Emo3D", an extensive "Text-Image-Expression dataset" spanning a wide spectrum of human emotions, each paired with images and 3D blendshapes. Leveraging Large Language Models (LLMs), we generate a diverse array of textual descriptions, facilitating the capture of a broad spectrum of emotional expressions. Using this unique dataset, we conduct a comprehensive evaluation of language-based models' fine-tuning and vision-language models like Contranstive Language Image Pretraining (CLIP) for 3D facial expression synthesis. We also introduce a new evaluation metric for this task to more directly measure the conveyed emotion. Our new evaluation metric, Emo3D, demonstrates its superiority over Mean Squared Error (MSE) metrics in assessing visual-text alignment and semantic richness in 3D facial expressions associated with human emotions. "Emo3D" has great applications in animation design, virtual reality, and emotional human-computer interaction.

本研究解决了现有3D面部情感建模中情感类别有限和数据集不足的问题。通过引入广泛的人类情感的“Emo3D”数据集，结合大语言模型生成多样的文本描述，实现对3D面部表情合成的全面评估。研究发现新评估指标Emo3D在视觉-文本一致性和3D面部表情的语义丰富性方面优于传统的均方误差（MSE）指标，具有广泛的应用潜力。

Emo3D：基于情感描述的3D面部表情生成的度量和基准数据集