Emo3D：用于情感描述生成3D面部表情的评估指标与基准数据集
摘要
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.
引用
@article{arxiv.2410.02049,
title = {Emo3D: Metric and Benchmarking Dataset for 3D Facial Expression Generation from Emotion Description},
author = {Mahshid Dehghani and Amirahmad Shafiee and Ali Shafiei and Neda Fallah and Farahmand Alizadeh and Mohammad Mehdi Gholinejad and Hamid Behroozi and Jafar Habibi and Ehsaneddin Asgari},
journal= {arXiv preprint arXiv:2410.02049},
year = {2024}
}
备注
11 pages, 10 figures