hand_pipeline

atharva-pantheon/hand_pipeline

hand_pipeline is a machine learning model from atharva-pantheon. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

A local, documented mirror of the Hugging Face model archive ggxxii/hand → handpipeline.zip.

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Updated Jul 11, 2026

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11 GB

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.pth4.7 GB · 42%

At a glance

Access: Public
Created: Jul 11, 2026
Updated: Jul 11, 2026
SHA: 944c360f

Model card

From the Hugging Face model README

hand-pipeline (mirror of `ggxxii/hand`)

A local, documented mirror of the Hugging Face model archive ggxxii/hand → hand_pipeline.zip.

What this is

An end-to-end monocular hand-motion recovery stack. Given a video, it detects, tracks, and reconstructs 3D hand motion (MANO parameters) using a temporal optimization built on Dyn-HaMR, fed by several off-the-shelf perception models.

Components

Area	Component	Role
Optimizer	Dyn-HaMR (`dyn-hamr/`)	Temporal MANO optimization; entry points `run_opt.py`, `run_human_opt.py`, `run_batch_opt.py`, `run_vis.py`
Hand recon	HaMeR (`third-party/hamer/`)	Per-frame hand mesh/MANO regressor
2D pose	ViTPose+ huge (`third-party/hamer/third-party/ViTPose/`)	Whole-body keypoints
Segmentation	SAM3 (`third-party/sam3/`)	Hand/subject masks
Camera / SLAM	ViPE + DROID-SLAM (`third-party/vipe/`, `_DATA/droid.pth`)	Camera pose / video pose estimation
Motion prior	HMP / NeMF (`_DATA/hmp_model/`)	Neural hand-motion prior
Body prior	human_body_prior (`third-party/human_body_prior/`)	VPoser-style prior utilities
Constraints	BMC (`_DATA/BMC/`)	Biomechanical bone-length / angle limits
Model	MANO (`_DATA/data/mano/MANO_RIGHT.pkl`)	Hand parametric model

Bundled weights (`_DATA/`, ~10.9 GB uncompressed)

File	Size	Model
`_DATA/vitpose_ckpts/vitpose+_huge/wholebody.pth`	3.81 GB	ViTPose+ huge
`_DATA/sam3_ckpt/sam3.pt`	3.45 GB	SAM3
`_DATA/hamer_ckpts/checkpoints/hamer.ckpt`	2.69 GB	HaMeR
`_DATA/hmp_model/results/model/optimizer.pth`	542 MB	HMP optimizer state
`_DATA/hmp_model/results/model/local_encoder.pth`	161 MB	HMP
`_DATA/hmp_model/results/model/nemf.pth`	93 MB	NeMF prior
`_DATA/BMC/joint_angles.npy`	90 MB	BMC stats
`_DATA/hamer_ckpts/hamer_demo_data.tar.gz`	17 MB	HaMeR demo data
`_DATA/hmp_model/results/model/global_encoder.pth`	17 MB	HMP
`_DATA/droid.pth`	16 MB	DROID-SLAM
`_DATA/data/mano/MANO_RIGHT.pkl`	3.8 MB	MANO
… + BMC/config/mean-std small files	<1 MB each

Total archive: 10.04 GB on disk, 11.06 GB uncompressed, 2501 files.

Layout

hand_pipeline/
├── dyn-hamr/         # optimization pipeline + run_*.py entry points
├── third-party/      # hamer, vipe, sam3, human_body_prior, actionclip
└── _DATA/            # all checkpoints & model files (see table above)

Sample inputs — companion repo `ggxxii/data_occ`

The matching input videos live in a separate HF repo: ggxxii/data_occ → data_occ.zip (557 MB, 188 files, 184 .mp4). It is occluded-hand test footage, not weights or code. Internal path: data/liuyufei/world2hand/data_occ/ (liuyufei = co-author Yufei Liu; world2hand = internal project name). Sources:

DexYCB — 1 clip
HoloAssist — 2 large egocentric clips (~95–137 MB)
Ropedia — ~180 short stereo clips (~1–5 MB each) ← ideal smoke-test inputs

Fetch it with:

curl -L "https://huggingface.co/ggxxii/data_occ/resolve/main/data_occ.zip" -o data_occ.zip
unzip data_occ.zip     # → data/liuyufei/world2hand/data_occ/{DexYCB,holoassist,ropedia}/

Setup / run (unverified — see caveat)

The archive ships no top-level README, requirements.txt, or documented run command of its own. Based on the structure, the intended flow is a Dyn-HaMR run pointed at the bundled _DATA:

cd hand_pipeline/dyn-hamr
# create env from Dyn-HaMR / HaMeR requirements (see third-party/hamer/setup.py)
python run_opt.py   # inspect argparse for --video / --data-root / --out flags

Caveat: these commands were not executed — no sample input or usage doc is included, and the exact CLI must be read from each run_*.py. Verify argparse args before running. Target env that matches the bundled weights: Python 3.11, CUDA 12.4 / PyTorch 2.4.x, ~11 GB VRAM headroom (fits an H100 80 GB comfortably; likely runs on ≥16 GB cards).

Created: Jul 11, 2026
Updated: Jul 11, 2026