llm-lab/Emo3D

Citation text from the card

@inproceedings{dehghani-etal-2025-emo3d,
    title = "{E}mo3{D}: Metric and Benchmarking Dataset for 3{D} Facial Expression Generation from Emotion Description",
    author = "Dehghani, Mahshid  and
      Shafiee, Amirahmad  and
      Shafiei, Ali  and
      Fallah, Neda  and
      Alizadeh, Farahmand  and
      Gholinejad, Mohammad Mehdi  and
      Behroozi, Hamid  and
      Habibi, Jafar  and
      Asgari, Ehsaneddin",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-naacl.173/",
    pages = "3158--3172",
    ISBN = "979-8-89176-195-7",
    abstract = "3D facial emotion modeling has important applications in areas such as animation design, virtual reality, and emotional human-computer interaction (HCI). However, existing models are constrained by limited emotion classes and insufficient datasets. To address this, we introduce Emo3D, an extensive {\textquotedblleft}Text-Image-Expression dataset{\textquotedblright} that spans 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, enabling the capture of a broad range of emotional expressions. Using this unique da

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