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Datasets:
LiKailin
/
HO-Tracker

Tasks:
Other
Modalities:
3D
Languages:
English
Tags:
hand-object
3d
python
License:
Dataset card Files
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kailin commited on
Commit
f60e946
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init release

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  1. .gitattributes +5 -0
  2. README.md +25 -0
  3. data/train_sample/h1o1/03ac9@0/right_hand.pkl +3 -0
  4. data/train_sample/h1o1/03ac9@0/right_obj.pkl +3 -0
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  6. data/train_sample/h1o1/03ac9@0/right_urdf/scan.urdf +3 -0
  7. data/train_sample/h1o1/05aa2@0/right_hand.pkl +3 -0
  8. data/train_sample/h1o1/05aa2@0/right_obj.pkl +3 -0
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  12. data/train_sample/h1o1/05d45@0/right_obj.pkl +3 -0
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  14. data/train_sample/h1o1/05d45@0/right_urdf/scan.urdf +3 -0
  15. data/train_sample/h1o1/07bb1@1/right_hand.pkl +3 -0
  16. data/train_sample/h1o1/07bb1@1/right_obj.pkl +3 -0
  17. data/train_sample/h1o1/07bb1@1/right_urdf/scan.ply +3 -0
  18. data/train_sample/h1o1/07bb1@1/right_urdf/scan.urdf +3 -0
  19. data/train_sample/h1o1/0b3a0@6/left_hand.pkl +3 -0
  20. data/train_sample/h1o1/0b3a0@6/left_obj.pkl +3 -0
  21. data/train_sample/h1o1/0b3a0@6/left_urdf/scan.ply +3 -0
  22. data/train_sample/h1o1/0b3a0@6/left_urdf/scan.urdf +3 -0
  23. data/train_sample/h1o1/0f4a7@1/left_hand.pkl +3 -0
  24. data/train_sample/h1o1/0f4a7@1/left_obj.pkl +3 -0
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  26. data/train_sample/h1o1/0f4a7@1/left_urdf/scan.urdf +3 -0
  27. data/train_sample/h1o1/0fb0e@2/right_hand.pkl +3 -0
  28. data/train_sample/h1o1/0fb0e@2/right_obj.pkl +3 -0
  29. data/train_sample/h1o1/0fb0e@2/right_urdf/scan.ply +3 -0
  30. data/train_sample/h1o1/0fb0e@2/right_urdf/scan.urdf +3 -0
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  32. data/train_sample/h1o1/15642@2/right_obj.pkl +3 -0
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  34. data/train_sample/h1o1/15642@2/right_urdf/scan.urdf +3 -0
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  38. data/train_sample/h1o1/178ef@1/right_urdf/scan.urdf +3 -0
  39. data/train_sample/h1o1/1be0e@1/right_hand.pkl +3 -0
  40. data/train_sample/h1o1/1be0e@1/right_obj.pkl +3 -0
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README.md ADDED
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+ # HO-Tracker Challenge — HANDS Workshop @ ICCV 2025
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+
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+ ## Dataset
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+
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+ Sample training data is provided in [`data/train_sample`](data/train_sample).
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+
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+ To browse the dataset locally:
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+
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+ ```bash
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+ # Step 1: Install dependencies
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+ pip install open3d==0.18.0
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+ pip install git+https://github.com/lixiny/manotorch.git
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+
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+ # Step 2: Download the MANO model from https://mano.is.tue.nl/downloads/
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+ # Place the extracted MANO assets under the `data/` directory
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+ # (e.g., `data/mano_v1_2`).
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+
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+ # Step 3: Launch the viewer
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+ python vis_demo.py
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+ ```
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+
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+ > Note (OakInk V2)
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+ >
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+ > In OakInk V2, MANO parameters are obtained by fitting to SMPL-X meshes. As a result, hand keypoints computed from MANO may differ from the original OakInk V2 keypoints by a few millimeters. Please choose the set that best suits your use case.
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