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LocalAI-io/sam-3d-body-dinov3-GGUF
sam-3d-body-dinov3-GGUF is a image-to-3d model from LocalAI-io. Use it for the image-to-3d task on the model card, and read the license before you ship it in a product. It is set up for gguf. The card lists the license as other.
F32 GGUF conversions of Meta's facebook/sam-3d-body-dinov3 for sam3d.cpp, a C++23/GGML CPU/Vulkan implementation. Given an image and a selected person's bounding box, the supported Body pose branch estimates a 3D body…
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From the Hugging Face model README
F32 GGUF conversions of Meta's facebook/sam-3d-body-dinov3 for sam3d.cpp, a C++23/GGML CPU/Vulkan implementation. Given an image and a selected person's bounding box, the supported Body pose branch estimates a 3D body mesh, joints, pose parameters and camera translation.
This is a format conversion and component selection, not a fine-tune or low-bit quantization. GGUF names a container format: these custom SAM3D architectures require sam3d.cpp, not llama.cpp or a generic LLM runner. There is no Python, PyTorch or CUDA inference dependency in the native runtime.
Download all three files together. The runtime validates companion identities.
| File | Content | Bytes |
|---|---|---|
body-dinov3-f32.gguf | Complete DINOv3 H+ image backbone, F32 | 3,363,058,592 |
body-pose-branch-f32.gguf | Body decoder/pose heads and topology, F32/I32 | 231,353,792 |
mhr-lod1-f32.gguf | MHR LOD1 geometry, skinning and correctives, F32/I32 | 693,111,264 |
The BF16 runtime mode converts selected weights/activations at load/execution;
it uses these same files. There are no Q4/Q8 variants in this release.
MANIFEST.json and SHA256SUMS identify the exact artifacts and conversion
source commit. The model source, metadata and license files are included in the
checksum manifest; no training checkpoints or reference photographs are bundled.
Use the Hugging Face CLI after this repository has been published:
hf download LocalAI-io/sam-3d-body-dinov3-GGUF --local-dir models/sam3d-body
(cd models/sam3d-body && sha256sum -c SHA256SUMS)
Build sam3d.cpp with CMake and an installed C++23 compiler; add a Vulkan SDK for GPU inference. Its optional Go web demo accepts these paths:
./build/sam3d-demo \
--listen 127.0.0.1:8097 --data generated/demo \
--runner build/debug/bin/sam3d-body-infer \
--backend CPU --module build/debug/bin/libggml-cpu.so \
--backbone models/sam3d-body/body-dinov3-f32.gguf \
--branch models/sam3d-body/body-pose-branch-f32.gguf \
--mhr models/sam3d-body/mhr-lod1-f32.gguf
The demo uses the same opaque C API available to library consumers. CPU debug builds prioritize sanitizer checks; use an optimized build for performance. See sam3d.cpp's README, API guide and demo README for build, Vulkan/BF16 and resource-limit configuration. It neither downloads models nor guesses a person detector. Remote webcam access requires HTTPS and a trusted deployment.
facebook/sam-3d-body-dinov3, revision
11aaa346c7204874a1cbafe3d39a979080b2c55a.facebookresearch/sam-3d-body, revision
b5c765a0d89d789985e186d396315e7590887b94.facebookresearch/dinov3, revision
6876159a11b4df116f30f667f8c9888617df0751.MANIFEST.json and embedded component metadata.The SAM/DINO weights are not Apache licensed. SAM material and its derivatives remain under the SAM License. DINOv3 material additionally retains the DINOv3 License. The MHR companion and topology retain MHR Apache-2.0, with applicable Momentum MIT notices. See NOTICE. The Apache grant for original sam3d.cpp code/documentation does not relicense the model or third-party adaptations.
SAM and DINO permit modification/distribution subject to their complete terms, including same-license redistribution, supplied license copies and use/legal/ trade-control restrictions. Do not use this conversion to evade upstream terms. Only process images you are authorized to use, obtain appropriate consent, and protect photographs and inferred body data. No endorsement by Meta is implied.
The original model card is authoritative for training data, intended use and research claims. Please acknowledge and cite SAM 3D Body: Robust Full-Body Human Mesh Recovery and MHR using the citations supplied by their original authors. These conversions make no independent training-data or benchmark claims.