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zzwalala/gvhmr-online
gvhmr-online is a machine learning model from zzwalala. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
Fine-tuned causal GVHMR for one person and a static camera. Uses the current frame and cached past information to predict the current body pose; no future frames or revision of earlier outputs.
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Updated Sep 16, 2026
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.ckpt163 MB · 100%
From the Hugging Face model README
Fine-tuned causal GVHMR for one person and a static camera. Uses the current frame and cached past information to predict the current body pose; no future frames or revision of earlier outputs.
Code and installation · Download checkpoint
The code is available on the online-gvhmr branch of the Otago repository (repository access required). The checkpoint download is public and verified without authentication.
After installing the code and frontend/body-model assets described in the repository README:
python tools/online/download_checkpoint.py --repo zzwalala/gvhmr-online
python tools/online/demo.py --video /path/to/video.mp4 \
--target-fps 10 --no-flip-test --fast --frontend-precision fp32 \
--save-meshes --output outputs/my_video
# Or show a live camera beside the predicted mesh:
python tools/online/preview.py --camera 0 --fps 10 --output outputs/my_camera
Run from Otago/online-gvhmr. No Hugging Face login is required to download. The checkpoint is inference-only; optimizer state, datasets, body-model assets and frontend weights are not included as separate assets.
87f5782cb36b2d4df1d806db0d8d085ca6496e8870529dd07b489ab22651e1cd.Matched H36M S9/S11 subset, 12 clips, six actions, 10 Hz. All models use identical detected RGB observations. 2,746/2,880 selected frames scored; 134 common detection failures excluded.
| Model | MPJPE (mm) ↓ | PA-MPJPE (mm) ↓ |
|---|---|---|
| Original offline | 61.29 | 43.05 |
| Original weights, causal | 65.50 | 46.81 |
| Fine-tuned causal | 61.97 | 42.89 |
Fine-tuning lowers causal MPJPE by 5.4%, while acceleration error increases 6.0%. This is not the full official benchmark. Root-relative pose accuracy does not measure global trajectory quality. Floor/world drift, occlusions, incorrect initial floor estimates, and moving cameras remain limitations.
Adapted from GVHMR. Retains the upstream educational/research/non-commercial terms in LICENSE. Use the original authors' citation from the repository. Separate frontend and body-model assets are subject to their own terms.