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--- |
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pretty_name: HO-Tracker Challenge |
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language: |
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- en |
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license: mit |
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task_categories: |
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- other |
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tags: |
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- hand-object |
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- 3d |
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- python |
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size_categories: |
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- n<10K |
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--- |
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# HO-Tracker Challenge — HANDS Workshop @ ICCV 2025 |
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## Dataset |
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Sample training data is provided in [`data/train_sample`](data/train_sample). |
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To browse the dataset locally: |
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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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# 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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# Step 3: Launch the viewer |
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python vis_demo.py |
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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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