README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - image-to-3d
tags:
  - hand-object-reconstruction
  - hand-pose-estimation
  - 3d-reconstruction
  - multi-view
  - synthetic
  - hand
  - MANO
pretty_name: HGGT Synthetic Dataset
configs:
  - config_name: preview
    data_files:
      - split: preview
        path: preview/**

HGGT Synthetic Dataset

This is the synthetic multi-view hand-object interaction dataset introduced in:

HGGT: Robust and Flexible 3D Hand Mesh Reconstruction from Uncalibrated Images
Yumeng Liu, Xiao-Xiao Long, Marc Habermann, Xuanze Yang, Cheng Lin, Yuan Liu, Yuexin Ma, Wenping Wang, Ligang Liu
[Paper] · [Project Page] · [Code]

The dataset contains diverse photorealistic hand-object interactions rendered with randomized camera viewpoints, providing critical viewpoint diversity absent in real-world captures. It is used together with real monocular and real multi-view data under a mixed-data training strategy.

Dataset Structure

catmint123/HGGT-synthetic-data/
├── small/
│   ├── shard_00000.tar   # ~2 GB each, contains ~50 object uuid dirs
│   ├── shard_00001.tar
│   └── ...
├── medium/
│   ├── shard_00000.tar
│   └── ...
├── large/
│   ├── shard_00000.tar
│   └── ...
├── splits/
│   ├── train.txt         # object uuids for training
│   └── test.txt          # object uuids for testing
└── invalid_masks_train.json

small, medium, and large follow the object scale categorization from GraspXL, where object meshes are grouped by their physical size. Each tar shard contains a set of object uuid directories with the following structure:

<object_uuid>/
└── <motion_id>/            # GraspXL motion sequence index (e.g. 1, 2, ...)
    └── sequence_<XXXXXX>/  # a sampled frame from the motion
        ├── images/         # multi-view rendered RGB images (000000.png, 000001.png, ...)
        ├── masks/          # hand and object segmentation masks per view
        ├── multi_depth_gt/ # ground-truth depth maps per view (.npz)
        ├── camera/
        │   ├── extrinsics/ # per-view camera extrinsic matrices (.txt)
        │   └── intrinsics.txt
        └── info.json       # metadata (motion path, frame id, MANO params, object paths)

Download

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="catmint123/HGGT-synthetic-data",
    repo_type="dataset",
    local_dir="data/hggt_synthetic",
)

To download a specific split only (e.g. small):

snapshot_download(
    repo_id="catmint123/HGGT-synthetic-data",
    repo_type="dataset",
    local_dir="data/hggt_synthetic",
    allow_patterns="small/*",
)

After downloading, extract the tar shards:

cd data/hggt_synthetic/small
for f in *.tar; do tar -xf "$f"; done

Usage

The splits/train.txt and splits/test.txt files list the object uuid directories used for training and testing respectively.

Please refer to the HGGT code repository for data loading and training scripts.

Data Sources

This dataset is built upon the following works:

• 3D object assets: Object meshes are sourced from Objaverse (Deitke et al., CVPR 2023), a large-scale dataset of 800K+ annotated 3D objects.
• Hand-object interaction data: Hand-object interaction configurations and grasping motions are sourced from GraspXL (Zhang et al., ECCV 2024), which provides large-scale grasping motion data for 500k+ objects with diverse dexterous hands.
• Hand texture materials: Hand appearance textures are sourced from DART (Gao et al., NeurIPS 2022), an articulated hand model with diverse accessories and rich textures.

Please make sure to also cite these works if you use this dataset.

License

This dataset is licensed under CC BY-NC 4.0. You are free to share and adapt the data for non-commercial purposes, provided appropriate credit is given.

This dataset builds upon Objaverse (CC BY 4.0), GraspXL (CC BY-NC 4.0), and DART. Please review and comply with their respective licenses when using this dataset.

Citation

@article{liu2026hggt,
  title={HGGT: Robust and Flexible 3D Hand Mesh Reconstruction from Uncalibrated Images},
  author={Liu, Yumeng and Long, Xiao-Xiao and Habermann, Marc and Yang, Xuanze and Lin, Cheng and Liu, Yuan and Ma, Yuexin and Wang, Wenping and Liu, Ligang},
  journal={arXiv preprint arXiv:2603.23997},
  year={2026}
}

@inproceedings{deitke2023objaverse,
  title={Objaverse: A universe of annotated 3d objects},
  author={Deitke, Matt and Schwenk, Dustin and Salvador, Jordi and Weihs, Luca and Michel, Oscar and VanderBilt, Eli and Schmidt, Ludwig and Ehsani, Kiana and Kembhavi, Aniruddha and Farhadi, Ali},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={13142--13153},
  year={2023}
}

@inProceedings{zhang2024graspxl,
  title={{GraspXL}: Generating Grasping Motions for Diverse Objects at Scale},
  author={Zhang, Hui and Christen, Sammy and Fan, Zicong and Hilliges, Otmar and Song, Jie},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2024}
}

@inproceedings{gao2022dart,
    title={{DART: Articulated Hand Model with Diverse Accessories and Rich Textures}},
    author={Daiheng Gao and Yuliang Xiu and Kailin Li and Lixin Yang and Feng Wang and Peng Zhang and Bang Zhang and Cewu Lu and Ping Tan},
    booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
    year={2022},
}