---
library_name: motius
pipeline_tag: video-to-video
license: other
tags:
  - human-motion
  - monocular-motion-capture
  - smpl
---

# GEM-SMPL

GEM-SMPL is the SMPL video-motion-estimation release of **GEM: A Generalist
Model for Human Motion**, originally released as GENMO.

- Paper: [GEM: A Generalist Model for Human Motion](https://arxiv.org/abs/2505.01425)
- Official source: [NVlabs/GENMO](https://github.com/NVlabs/GENMO)
- Motius checkpoint:
  [ZeyuLing/Motius-GEM-SMPL](https://huggingface.co/ZeyuLing/Motius-GEM-SMPL)
- Pinned source revision: `16bebf402d8893184249ee206d957b8248cd8310`
- Checkpoint SHA-256:
  `1d15cbe2864d6de61a75e83fdbfe83bec3c7b183eee3d3dcdbd9107e4456454a`

**Tasks:** Monocular Motion Capture

## Supported Tasks

| Task | Public API | Input | Output |
| --- | --- | --- | --- |
| Monocular Motion Capture | `infer_monocular_motion_capture` | RGB video | `MonocularCaptureResult` |

## Checkpoint

The Motius Hugging Face artifact contains the exact official GEM-SMPL,
HMR2, ViTPose, and YOLO checkpoint bytes and a manifest with every SHA-256.
The runtime source is shipped inside the `motius` wheel at its pinned revision;
inference never imports another repository checkout.

SMPL and SMPL-X files are license-gated and are not redistributed. Download
them into `checkpoints/body_models/` using
[`checkpoints/body_models/README.md`](https://github.com/ZeyuLing/Motius/blob/main/checkpoints/body_models/README.md).

## Motion Representation

GEM-SMPL predicts camera-space and gravity-aligned global body parameters.
Motius preserves the native 21-joint body pose, root orientation, translation,
and ten shape coefficients. Geometry materialization exposes:

- SMPL-24 named joints;
- 6,890-vertex SMPL meshes;
- camera and world root trajectories;
- per-frame camera intrinsics;
- the source video clock without temporal resampling.

The internal SMPL-X body layer is converted with the same fixed sparse
SMPL-X-to-SMPL map used by the pinned implementation.

## Usage

Create an isolated environment without cloning GENMO:

```bash
python3.10 -m venv outputs/envs/gem-smpl
outputs/envs/gem-smpl/bin/pip install -e ".[gem-smpl]"
```

Run the standard task API:

```python
from pathlib import Path

from motius.motion.representation.monocular_capture import (
    save_monocular_capture_result,
)
from motius.pipelines.gem_smpl import GemSmplPipeline

pipeline = GemSmplPipeline.from_pretrained(
    "ZeyuLing/Motius-GEM-SMPL",
    bundle_kwargs={
        "python_executable": "outputs/envs/gem-smpl/bin/python",
        "body_models_root": "checkpoints/body_models",
    },
)
result = pipeline.infer_monocular_motion_capture(
    "input.mp4",
    output_root="outputs/gem_smpl/run_001",
    materialize_geometry=True,
    render=True,
)
save_monocular_capture_result(
    result,
    Path("outputs/gem_smpl/run_001/result.npz"),
)
```

`render=True` requests the upstream in-camera/world previews. It can be left
off for evaluation and batch inference.

## Demo

This 768px, 30 FPS preview renders the world-space SMPL vertices returned by
the public Motius pipeline.

[![GEM-SMPL world-motion preview](https://raw.githubusercontent.com/ZeyuLing/Motius/main/assets/model_zoo/gem_smpl/gem_smpl_tennis_world.webp)](https://github.com/ZeyuLing/Motius/blob/main/assets/model_zoo/gem_smpl/gem_smpl_tennis_world.mp4)

## Evaluation Results

Protocol: `3dpw_test_camera_v1`, one inference item per official 3DPW person
track using the released target-crop protocol.

| Coverage | MPJPE ↓ | PA-MPJPE ↓ | Acceleration ↓ |
| ---: | ---: | ---: | ---: |
| 100.00% | 64.46 mm | 46.45 mm | 5.713 m/s² |

## Stage Parity

The migration gate replays the same video, checkpoint bytes, licensed body
models, precomputed visual tensors, and random seed through the pinned official
source and the Motius package.

| Boundary | Fields | Requirement | Result |
| --- | ---: | --- | --- |
| Tracking | 1 | exact | pass |
| Keypoints | 1 | exact | pass |
| Visual features | 2 | exact | pass |
| Complete model input | 15 | exact | pass |
| Network and decoded output | 9 | exact | pass |
| SMPL geometry | 4 | exact | pass |
| Public result | 10 | exact | pass |
| **Total** | **42** | **`rtol=0`, `atol=0`** | **pass** |

```bash
python tools/verify_monocular_pipeline_parity.py \
  --reference outputs/parity/gem_smpl/reference_trace.npz \
  --candidate outputs/parity/gem_smpl/motius_trace.npz
```

## License

The vendored source retains the NVIDIA OneWay Noncommercial license. Public
weights retain the NVIDIA Open Model License. SMPL and SMPL-X have separate
terms. See the
[GEM-SMPL attributions](https://github.com/ZeyuLing/Motius/blob/main/motius/models/gem_smpl/ATTRIBUTIONS.md)
and the packaged license files before use.

## Direct Loading

```python
from motius import Pipeline

pipeline = Pipeline.from_pretrained("ZeyuLing/Motius-GEM-SMPL")
```