4D-HOF: HO3D release assets

Checkpoint and prepared initialization for 4D-HOF: Hand-Object Flow Matching for Feed-Forward 4D Interaction Reconstruction.

Project page · Paper

Contents

• _DATA/ho3dv3_init.tar.gz: initialization bundles for 18 HO3D sequences.
• checkpoints/4dhof.pth: full flow matching checkpoint, provided uncompressed.
• _DATA/SHA256SUMS: SHA-256 checksums for the archive and checkpoint files.
• _DATA/ho3dv3_init.manifest.json: initialization file hashes, sequence list, and checkpoint hashes.

Download

Install the dependencies using bash setup_env.sh in the 4D-HOF code repository, then activate the environment with conda activate 4dhof. From the code repository root, run:

hf download eggplant71/4D-HOF --repo-type model \
  --include "_DATA/*" "checkpoints/*" --local-dir .
sha256sum -c _DATA/SHA256SUMS
tar -xzf _DATA/ho3dv3_init.tar.gz -C _DATA

For an existing environment, python -m pip install -r requirements.txt installs the Hugging Face download dependencies.

Extraction creates _DATA/ho3dv3_init/<sequence>/, containing contact.pkl, hand.pkl, intrinsics.pkl, mask.pkl, mesh.pkl, and pose.pkl. The checkpoint is downloaded directly to checkpoints/4dhof.pth.

Obtain MANO and HO3D separately. Place MANO_RIGHT.pkl at _DATA/mano/MANO_RIGHT.pkl as described in the code repository. Only load trusted pickle and checkpoint files.

Run

From the code repository root:

python infer_ho3d.py --ho3d-root /path/to/HO3D_v3
python eval_ho3d.py --ho3d-root /path/to/HO3D_v3