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initialneil/
sapiens-omni-600

Name: sapiens-omni-600
Availability: InStock

pytorch

smpl-x

human-pose-estimation

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sapiens-omni-600

Monocular SMPL-X whole-body pose & shape from dense 600-vertex omni surface landmarks. A Sapiens 0.6B finetune (COCO-WholeBody 133 → omni-600 head) that predicts 600 dense SMPL-X surface landmarks from a single RGB image, then fits SMPL-X to them with a Theseus 2D-reprojection optimizer.

Code & full usage docs: https://github.com/initialneil/sapiens-omni-600
Landmark partition: 600 SMPL-X vertex indices from OmniFit

Results (AGORA-val)

Method	N	PA-MVE (mm) ↓
SMPLest-X H (published SoTA)	500	124.5
SMPLer-X L32	500	133.4
v5 + SMPLest-X init (this model)	396	109.7
v5 (heatmap fitter, no init)	500	140.4

Beats SMPLest-X by 12% on the matched record set; 2D landmark accuracy ~5× better.

Files

Path	Iter	Notes
`v5/iter_150000.pth`	150000	Released heatmap-only model (the headline). Full training checkpoint (state_dict + optimizer).

v6 (adds a per-landmark depth head for a 3D-aware fitter) is in training and not yet released here.

Usage

bash

git clone https://github.com/initialneil/sapiens-omni-600.git omni_pose
cd omni_pose && bash install/setup.sh

hf download initialneil/sapiens-omni-600 v5/iter_150000.pth --local-dir data/checkpoints/omni600

python scripts/eval/run_omni_pose_agora.py \
    --config     configs/omni_pose/bedlam2/sapiens_0.6b-omni600-bedlam2-1024x768.py \
    --checkpoint data/checkpoints/omni600/v5/iter_150000.pth \
    --n 500 --out tmp/omni_pose_v5

See the GitHub README for the SMPLest-X-init flow (the 109.7 mm headline) and v6.

License

License is TBD / research-only pending finalization. SMPL-X model files (required, downloaded separately) are non-commercial research only. Do not redistribute until a formal license is in place.