PALM: A Dataset and Baseline for Learning Multi-subject Hand Prior (Paper)

Zicong Fan, Edoardo Remelli, David Dimond, Fadime Sener, Liuhao Ge, Bugra Tekin, Cem Keskin, Shreyas Hampali

News

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• 2025.11.07: PALM is accepted to 3DV'26!

This is a repository for PALM, a large-scale dataset comprising calibrated multi-view high-resolution RGB images and 3dMD hand scans (a). It features 263 subjects spanning a wide range of skin tones and hand sizes, 90k RGB images, and 13k high-quality hand scans with corresponding MANO registrations (b). This diversity and precision provide a foundation for learning a universal prior over human hand shape and appearance.

Why use PALM?

Summary on dataset:

• 90k multi-view RGB images
• Accurate MANO registration
• 263 subjects
• 7 RGB views
• 2448 x 2048 high resolution images
• 13k 3dMD hand scans

Potential directions from PALM:

• Synthetic data for hand pose estimation
• Hand personalization
• Learning high-resolution hand foundation models
• Generative model of hand with diverse skin tone and texture
• Naive use case: Unwrap UV texture from RGB images for downstream tasks

Features

• Instructions to download the PALM dataset
• Scripts to load and visualize PALM dataset
• Code to leverage our multi-subject hand prior

Getting started

Get a copy of the code and set permissions:

git clone https://github.com/facebookresearch/PALM.git
cd PALM; git submodule update --init --recursive
cd code; chmod +x *.sh

• Setup environment: see docs/setup.md
• Data documentation (downloads, checkpoints, dataset, log folder): see docs/data_doc.md

If you only want to use the data: Follow our instructions here docs/palm.md

If you want to use the model:

• Put PALM under ./code/load/PALM/XR20B/folders/SUBJECT_ID/ for dataset path (see configs/dataset/prior.yaml)
• Training prior on PALM or to optimize PALM for personalization and relighting: see docs/palmnet.md
• Preprocess custom images for PALM-Net personalization: see generator/README.md
• Evaluation on InterHand2.6M: see docs/interhand.md

Official Citation

@inproceedings{fan2026palm,
  title={{PALM}: A Dataset and Baseline for Learning Multi-subject Hand Prior},
  author={Fan, Zicong and Remelli, Edoardo and Dimond, David and Sener, Fadime and Ge, Liuhao and Tekin, Bugra and Keskin, Cem and Hampali, Shreyas},
  booktitle={2026 International Conference on 3D Vision (3DV)},
  year={2026},
  organization={IEEE}
}

Contact

For technical questions, please create an issue. For other questions, please contact the first author.

Acknowledgments

The authors would like to thank: Xu Chen for feedback on SNARF, Quoc Nguyen for compute support.

Our code benefits a lot from IntrinsicAvatar, SNARF, FastSNARF. If you find our work useful, consider checking out and citing the following papers:

@inproceedings{WangCVPR2024,
  title   = {IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing},
  author  = {Shaofei Wang and Bo\v{z}idar Anti\'{c} and Andreas Geiger and Siyu Tang},
  booktitle = {IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  year    = {2024}
}

@article{Chen2023PAMI,
  author = {Xu Chen and Tianjian Jiang and Jie Song and Max Rietmann and Andreas Geiger and Michael J. Black and Otmar Hilliges},
  title = {Fast-SNARF: A Fast Deformer for Articulated Neural Fields},
  journal = {Pattern Analysis and Machine Intelligence (PAMI)},
  year = {2023}
}

@inproceedings{chen2021snarf,
  title={SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes},
  author={Chen, Xu and Zheng, Yufeng and Black, Michael J and Hilliges, Otmar and Geiger, Andreas},
  booktitle={International Conference on Computer Vision (ICCV)},
  year={2021}
}

License

The majority of PALM is licensed under CC-BY-NC, however portions of the project are available under separate license terms: IntrinsicAvatar is licensed under the MIT license.