SMPLX Anything
smplx_anything is a unified-format preprocessed bundle (processed_*_resized) of four SMPL-X-based 3D human datasets, packaged into a single tar archive for easy distribution.
Sub-datasets
| Sub-dataset | Folder | Project page | Paper |
|---|---|---|---|
| AGORA | processed_agora_resized/ |
agora.is.tue.mpg.de | Patel et al., CVPR 2021 |
| Anny-One | processed_annyone_resized/ |
Anny-One @ NAVER LABS Europe · GitHub | Baradel et al., 2025 |
| BEDLAM | processed_bedlam_resized/ |
bedlam.is.tue.mpg.de | Black et al., CVPR 2023 |
| BEDLAM 2.0 | processed_bedlam2_resized/ |
bedlam2.is.tue.mpg.de | BEDLAM 2.0, NeurIPS 2025 |
Each sub-dataset retains the license of its original authors. Please review and comply with the original licenses before use.
File layout
The full bundle is packaged into a single tar archive and split into 40 GB chunks for upload.
smplx_anything.tar.aa
smplx_anything.tar.ab
smplx_anything.tar.ac
...
SHA256SUMS # integrity checksums for each split part
README.md
The split-file suffixes (aa, ab, ...) follow GNU split's default scheme.
Download
1) Using the hf CLI (recommended)
pip install -U "huggingface_hub>=1.0"
hf download Yong-Hoon/smplx_anything \
--repo-type dataset \
--local-dir ./smplx_anything
The legacy
huggingface-cliwas deprecated in v1.0 and replaced byhf. If you must use the old CLI:huggingface-cli download Yong-Hoon/smplx_anything --repo-type dataset --local-dir ./smplx_anything
2) Using git lfs
git lfs install
git clone https://huggingface.co/datasets/Yong-Hoon/smplx_anything
3) Downloading only some parts
hf download Yong-Hoon/smplx_anything \
smplx_anything.tar.aa smplx_anything.tar.ab \
--repo-type dataset \
--local-dir ./smplx_anything
Integrity check (optional)
cd ./smplx_anything
sha256sum -c SHA256SUMS
Extraction
Concatenate the split parts and pipe them straight into tar. You do not need to first reassemble a single .tar file on disk.
cd ./smplx_anything
cat smplx_anything.tar.* | tar -xvf -
After extraction, the following four folders will appear:
processed_agora_resized/
processed_annyone_resized/
processed_bedlam_resized/
processed_bedlam2_resized/
If you are tight on disk space, you can delete the split parts after extraction. However, in case extraction fails midway, we recommend running
sha256sum -c SHA256SUMSfirst and only deleting the parts after a clean extraction.
If you prefer a single .tar file
cat smplx_anything.tar.* > smplx_anything.tar
tar -xvf smplx_anything.tar
Windows users
Concatenate the split parts in PowerShell, then extract with 7-Zip or WinRAR.
Get-Content .\smplx_anything.tar.* -Raw -Encoding Byte | Set-Content .\smplx_anything.tar -Encoding Byte
# Then extract smplx_anything.tar with 7-Zip
How the splits were produced (reproducibility)
The uploaded split files were created with the commands below.
# Bundle the four folders into one tar stream and split into 40 GB chunks
tar -cf - \
processed_agora_resized \
processed_annyone_resized \
processed_bedlam2_resized \
processed_bedlam_resized \
| split -b 40G - smplx_anything.tar.
# Generate integrity checksums
sha256sum smplx_anything.tar.* > SHA256SUMS
tar -cf -— bundle the four folders into a tar stream on stdout (no compression).split -b 40G -— read stdin and split it into 40 GB chunks; suffixes default toaa,ab, ...- Output file prefix:
smplx_anything.tar.
No additional compression is applied at the tar level: the underlying media is already compressed (images, etc.), so further compression yields little gain and slows down extraction.
Citation
Please cite the original paper for each sub-dataset you use.
- AGORA: Patel et al., AGORA: Avatars in Geography Optimized for Regression Analysis, CVPR 2021. — Project page
- Anny-One / Anny: Baradel et al., Human Mesh Modeling for Anny Body, 2025. — Project page · Code
- BEDLAM: Black et al., BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion, CVPR 2023. — Project page
- BEDLAM 2.0: BEDLAM 2.0: Synthetic Humans and Cameras in Motion, NeurIPS 2025 (Datasets & Benchmarks). — Project page
Contact
For dataset-related issues, please use the Discussions tab on this repository.
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