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SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark

Project README

SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark

Zhengdi Yu1,2 · Shaoli Huang2 · Yongkang Cheng2 · Tolga Birdal1

1Imperial College London, 2Tencent AI Lab

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SignAvatars is the first large-scale 3D sign language holistic motion dataset with mesh annotations, which comprises 8.34M precise 3D whole-body SMPL-X annotations, covering 70K motion sequences. The corresponding MANO hand version is also provided.

News :triangular_flag_on_post:

  • [2023/11/2] Paper is now available. ⭐

Dataset description

Dataset download

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Application examples on SLP

Blender Blender
SLP from HamNoSys SLP from Word
Blender Blender
SLP from ASL SLP from GSL

Instruction

Coming soon!

Citation

@inproceedings{yu2023signavatars,
  title = {SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark},
  author = {Yu, Zhengdi and Huang, Shaoli and Cheng, Yongkakng and Birdal, Tolga},
  journal = {arXiv preprint arXiv:2310.20436},
  month     = {November},
  year      = {2023}
  }

Contact

For technical questions, please contact [email protected]

Open Source Agenda is not affiliated with "SignAvatars" Project. README Source: ZhengdiYu/SignAvatars

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