Emo3D: Metric and Benchmarking Dataset for 3D Facial Expression Generation from Emotion Description
<|reference_start|>Emo3D: Metric and Benchmarking Dataset for 3D Facial Expression Generation from Emotion Description: 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.<|reference_end|>
@article{dehghani2024emo3d:,
    title={Emo3D: Metric and Benchmarking Dataset for 3D Facial Expression
  Generation from Emotion Description},
    author={Mahshid Dehghani, Amirahmad Shafiee, Ali Shafiei, Neda Fallah,
  Farahmand Alizadeh, Mohammad Mehdi Gholinejad, Hamid Behroozi, Jafar Habibi,
  Ehsaneddin Asgari},
    journal={arXiv preprint arXiv:2410.02049},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02049},
    primaryClass={cs.CV cs.CL cs.GR}
}