Instructions to use KitsuMate/sam-3d-body-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SAM 3D Body
How to use KitsuMate/sam-3d-body-onnx with SAM 3D Body:
from notebook.utils import setup_sam_3d_body estimator = setup_sam_3d_body(KitsuMate/sam-3d-body-onnx) outputs = estimator.process_one_image(image) rend_img = visualize_sample_together(image, outputs, estimator.faces)
- Notebooks
- Google Colab
- Kaggle
SAM 3D Body ONNX
ONNX Runtime exports of SAM 3D Body, organized under onnx/<variant>/.
Available variants
| Variant | Path | Upstream revision | Precision |
|---|---|---|---|
| ViT-H | onnx/vith/ |
f7f4ba4f67739e297f291639afb7c59041745367 |
FP16 backbone + FP32 decoder/head graphs |
DINOv3 is not published yet. Once it passes the same export, parity, and
accelerator gates, it will use the sibling path onnx/dinov3/ in this same
repository. ViT-H and DINOv3 need distinct ONNX files because their backbones,
weights, and tensor contracts differ; they do not need separate repositories.
The ViT-H export contains 22 role-named ONNX graphs and four NumPy-native MHR
artifacts. Decoder layers are separate because MHR projection and keypoint
updates are interleaved between recurrent decoder steps. The exact interfaces,
sizes, and SHA-256 hashes are recorded in onnx/vith/manifest.json.
Download the variant with:
hf download KitsuMate/sam-3d-body-onnx --include "onnx/vith/*" --local-dir .
This is a split-runtime export rather than one standalone ONNX graph. Consumers
must follow the role schedule in the manifest and implement the documented
NumPy preprocessing, camera, MHR, and hand-fusion steps. CPU golden parity is
recorded in onnx/vith/parity_report.json; source identities and exported-file
hashes are in onnx/vith/PROVENANCE.json.
See LICENSE for the model terms and NOTICE.md for third-party attribution.
Model tree for KitsuMate/sam-3d-body-onnx
Base model
facebook/sam-3d-body-vith