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Update paper title in citation

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  1. croissant.json +1 -1
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@@ -59,7 +59,7 @@
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  "MySign-2026"
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  ],
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  "description": "MySign is a 3D motion-capture dataset of Bahasa Isyarat Malaysia (Malaysian Sign Language, BIM) for fine-grained sign language generation and recognition. It comprises 5,000 isolated-sign instances (5 Deaf native signers x 1,000 BIM Sign Bank glosses, fully balanced with no missing entries), captured at 200 Hz with a six-camera OptiTrack system plus MANUS Prime 3 Data Gloves and retargeted to the SMPL-X body model, totaling approximately 15.57M synchronized frames and 36 hours of recording. The 1,000-gloss vocabulary spans nine main categories (conversation, culture, daily-life, general, health, nature, people, things, time) and 46 subcategories. Each instance is anchored to an authorized BIM Sign Bank entry at capture time, so the gloss label is community-sanctioned by construction rather than by post-hoc rating. The release uses Filmbox (.fbx) skeletal animation, organized as Signer001/ ... Signer005/. metadata.csv is an index over the .fbx files (file_name, gloss, signer_id, take). A signer-independent train/test split is provided (4 signers train, 1 test). All recordings are skeletal-only (no video, audio, or facial texture).",
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- "citeAs": "@misc{mysign2026,\n title = {MySign: A 3D Motion-Capture Dataset of Malaysian Sign Language},\n author = {{mysigner}},\n year = {2026},\n howpublished = {Hugging Face Datasets},\n url = {https://huggingface.co/datasets/mysigner/MySign},\n note = {CC BY-NC-SA 4.0}\n}",
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  "url": "https://huggingface.co/datasets/mysigner/MySign",
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  "sameAs": "https://huggingface.co/datasets/mysigner/MySign",
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  "license": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
 
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  "MySign-2026"
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  ],
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  "description": "MySign is a 3D motion-capture dataset of Bahasa Isyarat Malaysia (Malaysian Sign Language, BIM) for fine-grained sign language generation and recognition. It comprises 5,000 isolated-sign instances (5 Deaf native signers x 1,000 BIM Sign Bank glosses, fully balanced with no missing entries), captured at 200 Hz with a six-camera OptiTrack system plus MANUS Prime 3 Data Gloves and retargeted to the SMPL-X body model, totaling approximately 15.57M synchronized frames and 36 hours of recording. The 1,000-gloss vocabulary spans nine main categories (conversation, culture, daily-life, general, health, nature, people, things, time) and 46 subcategories. Each instance is anchored to an authorized BIM Sign Bank entry at capture time, so the gloss label is community-sanctioned by construction rather than by post-hoc rating. The release uses Filmbox (.fbx) skeletal animation, organized as Signer001/ ... Signer005/. metadata.csv is an index over the .fbx files (file_name, gloss, signer_id, take). A signer-independent train/test split is provided (4 signers train, 1 test). All recordings are skeletal-only (no video, audio, or facial texture).",
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+ "citeAs": "@misc{mysign2026,\n title = {MySign: A High-Fidelity Motion-Capture Dataset for 3D Sign Generation in Bahasa Isyarat Malaysia},\n author = {{mysigner}},\n year = {2026},\n howpublished = {Hugging Face Datasets},\n url = {https://huggingface.co/datasets/mysigner/MySign},\n note = {CC BY-NC-SA 4.0}\n}",
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  "url": "https://huggingface.co/datasets/mysigner/MySign",
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  "sameAs": "https://huggingface.co/datasets/mysigner/MySign",
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  "license": "https://creativecommons.org/licenses/by-nc-sa/4.0/",