{
 "id": "llm-lab/Emo3D",
 "owner": "llm-lab",
 "name": "Emo3D",
 "downloads": 36,
 "likes": 1,
 "last_modified": "2025-05-04T17:38:11.000Z",
 "stated_licence": "mit",
 "stated_from": "tag",
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 "citation": "@inproceedings{dehghani-etal-2025-emo3d,\n    title = \"{E}mo3{D}: Metric and Benchmarking Dataset for 3{D} Facial Expression Generation from Emotion Description\",\n    author = \"Dehghani, Mahshid  and\n      Shafiee, Amirahmad  and\n      Shafiei, Ali  and\n      Fallah, Neda  and\n      Alizadeh, Farahmand  and\n      Gholinejad, Mohammad Mehdi  and\n      Behroozi, Hamid  and\n      Habibi, Jafar  and\n      Asgari, Ehsaneddin\",\n    editor = \"Chiruzzo, Luis  and\n      Ritter, Alan  and\n      Wang, Lu\",\n    booktitle = \"Findings of the Association for Computational Linguistics: NAACL 2025\",\n    month = apr,\n    year = \"2025\",\n    address = \"Albuquerque, New Mexico\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.findings-naacl.173/\",\n    pages = \"3158--3172\",\n    ISBN = \"979-8-89176-195-7\",\n    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",
 "signal": "permissive",
 "reason": "The declared licence (mit) is classified as permissive.",
 "revision": "90f4b2dd667bfa90826c5f48b14b370c5403ad45",
 "stated_evidence": "https://huggingface.co/datasets/llm-lab/Emo3D/blob/90f4b2dd667bfa90826c5f48b14b370c5403ad45/README.md",
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}