| --- |
| license: mit |
| tags: |
| - 4d-hof |
| - ho3d |
| - hand-object-reconstruction |
| library_name: pytorch |
| --- |
| |
| # 4D-HOF: HO3D release assets |
|
|
| Checkpoint and prepared initialization for **4D-HOF: Hand-Object Flow Matching for Feed-Forward 4D Interaction Reconstruction**. |
|
|
| [Project page](https://tamu-visual-ai.github.io/4D-HOF/) · [Paper](https://arxiv.org/abs/2610.08782) |
|
|
| ## 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: |
|
|
| ```bash |
| 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: |
|
|
| ```bash |
| python infer_ho3d.py --ho3d-root /path/to/HO3D_v3 |
| python eval_ho3d.py --ho3d-root /path/to/HO3D_v3 |
| ``` |
|
|
