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RunyiY
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HandWM

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Tags:
hands
egocentric-video
imu
mano
world-model
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metadata 
pretty_name: HandWM
license: other
license_name: mixed
tags:
  - hands
  - egocentric-video
  - imu
  - mano
  - world-model
size_categories:
  - 100K<n<1M
viewer: false

HandWM

Everything needed to run the HandWM code (hand motion → simulated glove IMU) and the P3-WAM + IMU re-implementation on EgoDex and ARCTIC: the datasets, MANO, intermediate assets and both code trees. organize.sh turns a download into the directory layout the code expects; see ORGANIZE.md.

hf download RunyiY/HandWM --repo-type dataset --local-dir handwm_download
bash handwm_download/organize.sh handwm_download /path/to/root

Contents

Path Size What
data/egodex/<part>/*.tar 2.11 TB EgoDex, all 338,234 episodes (.hdf5 poses + .mp4 video), ~10 GB tar shards; members are paths relative to raw/egodex
data/arctic/images_zips/sXX/*.zip 696 GB ARCTIC images, 339 sequences, 10 subjects (s03 is the official test subject, no public annotations)
data/arctic/arctic_unpacked.tar 0.8 GB ARCTIC raw_seqs (MANO, SMPL-X, object, egocentric camera; 301 sequences), meta, splits_json, mano_v1_2
data/mano/MANO_{LEFT,RIGHT}.pkl 10 MB MANO v1.2 hand models, chumpy-free copies loadable with smplx
data/p3wam/egodex_mano.tar 23 GB MANO fits of 162,836 EgoDex episodes (P3-WAM hand targets)
data/p3wam/codec_v2/codec.pt 42 MB frozen action codec (P3-WAM)
data/p3wam/imu-stage-low/new_params.pt 2.2 GB IMU warm-up weights for the low-noise expert (train_wam.py --init-new)
code/handswm, code/p3wam_imu <0.2 GB code, configs, tests, docs, evaluation results
manifest/ egodex_index.csv.gz (file → shard), egodex_fitted_files.txt (P3-WAM training subset), files.csv.gz (sha256 of every payload file)
data/*/receipts/ download receipts of the original archives

Not included: the Wan2.2-Fun-A14B-Control backbone (Apache-2.0, organize.sh downloads it from alibaba-pai/Wan2.2-Fun-A14B-Control), simulated IMU and canonical motion (regenerated by the code), training checkpoints.

Licenses

Each component keeps its upstream license. All of them are non-commercial.

Component License Source
EgoDex CC BY-NC-ND 4.0, files unchanged (repackaged into tar archives only) Apple, https://github.com/apple-aiml-research/ml-egodex
ARCTIC (images, annotations, meta) ARCTIC Data & Software license, non-commercial scientific research https://arctic.is.tue.mpg.de
MANO models MANO license, non-commercial scientific research https://mano.is.tue.mpg.de
EgoDex MANO fits derived from EgoDex and MANO; their terms apply this work
Code, codec and IMU weights research use this work

Before using ARCTIC or MANO, register and accept their licenses on the sites above.

References

  • EgoDex: R. Hoque et al., EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video, 2025.
  • ARCTIC: Z. Fan et al., ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation, CVPR 2023.
  • MANO: J. Romero, D. Tzionas, M. J. Black, Embodied Hands: Modeling and Capturing Hands and Bodies Together, SIGGRAPH Asia 2017.
  • P3-WAM: https://p3-wam.pages.dev/