DATASET_CARD.md

---
license: cc-by-4.0
task_categories:
  - robotics
  - reinforcement-learning
tags:
  - motion-capture
  - humanoid-robot
  - unitree-g1
  - bvh
  - sim-to-real
size_categories:
  - n<1K
---

# G1 Moves

59 motion capture clips for the Unitree G1 humanoid robot (29 DOF, mode 15), captured with [MOVIN TRACIN](https://movin3d.com/) markerless motion capture. Each clip is provided at multiple pipeline stages: raw mocap (BVH/FBX), retargeted robot joint trajectories (PKL), and processed RL training data (NPZ).

## Dataset Summary

| | |
|---|---|
| **Total clips** | 59 |
| **Total duration** | 29.5 minutes (106,149 frames at 60 FPS) |
| **Categories** | Dance (28), Karate (27), Bonus (4) |
| **Clip duration** | 6.5s - 119.5s |
| **Robot** | Unitree G1, mode 15, 29 DOF |
| **Capture system** | MOVIN TRACIN (markerless, LiDAR + vision) |
| **Retarget SDK** | [movin_sdk_python](https://github.com/MOVIN3D/movin_sdk_python) |

## Supported Tasks

- **Motion imitation RL**: Train policies to track reference motions on the G1 robot using the NPZ training data
- **Sim-to-real transfer**: Deploy trained policies from MuJoCo simulation to physical G1 hardware
- **Motion retargeting research**: Study human-to-robot motion transfer using the BVH-to-PKL pipeline
- **Animation / visualization**: Import FBX files into Blender, Maya, Unreal Engine, or Unity

## Dataset Structure

Each clip lives in its own subfolder organized by pipeline stage:

```
<category>/<clip>/
  capture/                Original motion capture data
    <clip>.bvh            BVH motion capture (51-joint humanoid skeleton)
    <clip>.gif            Preview animation
    <clip>.mp4            Preview video
    <clip>_bl.fbx         FBX for Blender
    <clip>_mb.fbx         FBX for Maya
    <clip>_ue.fbx         FBX for Unreal Engine
    <clip>_un.fbx         FBX for Unity
  retarget/               Retargeted G1 joint trajectories
    <clip>.pkl            Retargeted joint angles (29 DOF)
    <clip>.csv            Joint angles as CSV (no header)
    <clip>_retarget.gif   Retarget preview animation
    <clip>_retarget.mp4   Retarget preview video
  training/               Processed RL training data
    <clip>.npz            Training data with forward kinematics
    <clip>_training.gif   Training visualization
    <clip>_training.mp4   Training visualization video
  policy/                 Trained RL policy (if available)
    <clip>_policy.pt      PyTorch checkpoint
    <clip>_policy.gif     Policy rollout animation
    <clip>_policy.mp4     Policy rollout video
    agent.yaml            PPO hyperparameters
    env.yaml              Full environment configuration
    training_log.csv      Training metrics (rewards, losses, errors)
```

## File Format Reference

### BVH (capture)

Standard BVH motion capture format with a 51-joint humanoid skeleton.

- **Root**: Hips (6 DOF: XYZ position + YXZ Euler rotation)
- **Joints**: 3 DOF each (YXZ Euler rotation order)
- **Frame rate**: 60 FPS
- **Coordinate system**: Y-up

### PKL (retarget)

Python pickle containing a dict with retargeted G1 joint trajectories:

| Key | Shape | Type | Description |
|-----|-------|------|-------------|
| `fps` | scalar | int | Frame rate (60) |
| `root_pos` | (N, 3) | float64 | Root position in world frame (meters) |
| `root_rot` | (N, 4) | float64 | Root orientation as quaternion (xyzw) |
| `dof_pos` | (N, 29) | float64 | Joint angles in radians |

Joint order (29 DOF):

| Index | Joint | Index | Joint |
|-------|-------|-------|-------|
| 0 | left_hip_pitch | 15 | left_shoulder_pitch |
| 1 | left_hip_roll | 16 | left_shoulder_roll |
| 2 | left_hip_yaw | 17 | left_shoulder_yaw |
| 3 | left_knee | 18 | left_elbow |
| 4 | left_ankle_pitch | 19 | left_wrist_roll |
| 5 | left_ankle_roll | 20 | left_wrist_pitch |
| 6 | right_hip_pitch | 21 | left_wrist_yaw |
| 7 | right_hip_roll | 22 | right_shoulder_pitch |
| 8 | right_hip_yaw | 23 | right_shoulder_roll |
| 9 | right_knee | 24 | right_shoulder_yaw |
| 10 | right_ankle_pitch | 25 | right_elbow |
| 11 | right_ankle_roll | 26 | right_wrist_roll |
| 12 | waist_yaw | 27 | right_wrist_pitch |
| 13 | waist_roll | 28 | right_wrist_yaw |
| 14 | waist_pitch | | |

### CSV (retarget)

Same data as PKL in plain CSV format (no header row). 36 columns:

| Columns | Content |
|---------|---------|
| 0-2 | Root position (x, y, z) |
| 3-6 | Root quaternion (x, y, z, w) |
| 7-35 | Joint angles (29 DOF, same order as PKL) |

### NPZ (training)

NumPy compressed archive with forward kinematics computed from the retargeted motion. Used directly as RL training reference.

| Key | Shape | Type | Description |
|-----|-------|------|-------------|
| `fps` | (1,) | float64 | Frame rate (60) |
| `joint_pos` | (N, 29) | float32 | Joint positions (radians) |
| `joint_vel` | (N, 29) | float32 | Joint velocities (rad/s) |
| `body_pos_w` | (N, 30, 3) | float32 | Body positions in world frame (meters) |
| `body_quat_w` | (N, 30, 4) | float32 | Body orientations as quaternions |
| `body_lin_vel_w` | (N, 30, 3) | float32 | Body linear velocities (m/s) |
| `body_ang_vel_w` | (N, 30, 3) | float32 | Body angular velocities (rad/s) |

N = BVH frames - 1 (velocity requires finite differences).

### FBX (capture)

Four platform-optimized FBX variants per clip:

| Suffix | Target |
|--------|--------|
| `_bl.fbx` | Blender |
| `_mb.fbx` | Maya |
| `_ue.fbx` | Unreal Engine |
| `_un.fbx` | Unity |

## Usage Examples

### Load retargeted motion (PKL)

```python
import pickle
import numpy as np

with open("dance/B_DadDance/retarget/B_DadDance.pkl", "rb") as f:
    motion = pickle.load(f)

print(f"FPS: {motion['fps']}")
print(f"Duration: {motion['dof_pos'].shape[0] / motion['fps']:.1f}s")
print(f"Root position at frame 0: {motion['root_pos'][0]}")
print(f"Joint angles shape: {motion['dof_pos'].shape}")  # (2509, 29)
```

### Load training data (NPZ)

```python
import numpy as np

data = np.load("dance/B_DadDance/training/B_DadDance.npz")

joint_pos = data["joint_pos"]    # (2508, 29)
joint_vel = data["joint_vel"]    # (2508, 29)
body_pos = data["body_pos_w"]    # (2508, 30, 3)
body_quat = data["body_quat_w"]  # (2508, 30, 4)

# Get pelvis height over time
pelvis_z = body_pos[:, 0, 2]
print(f"Pelvis height: {pelvis_z.min():.3f} - {pelvis_z.max():.3f} m")
```

### Filter clips by duration or difficulty

```python
import json

with open("manifest.json") as f:
    manifest = json.load(f)

# Find clips longer than 30 seconds
long_clips = {
    name: clip for name, clip in manifest["clips"].items()
    if clip["duration_s"] > 30
}
print(f"{len(long_clips)} clips > 30s")

# Sort by motion energy (difficulty proxy)
by_energy = sorted(
    manifest["clips"].items(),
    key=lambda x: x[1]["motion_stats"]["mean_joint_velocity"],
    reverse=True,
)
print("Most energetic:", by_energy[0][0])
print("Least energetic:", by_energy[-1][0])
```

## Data Collection

All 59 clips were captured using the [MOVIN TRACIN](https://movin3d.com/) markerless motion capture system. MOVIN TRACIN uses on-device AI to fuse LiDAR point clouds and vision into motion data without markers, suits, or multi-camera rigs. Performances were recorded and exported using [MOVIN Studio](https://www.movin3d.com/studio).

### Performers

| Prefix | Performer | Clips |
|--------|-----------|-------|
| `B_` | [Mitch Chaiet](https://mitchchaiet.com/) | Bonus clips + some dance |
| `J_` | [Jasmine Coro](https://jasminecoro.com/) | Dance choreography |
| `M_` | [Mike Gassaway](https://www.backstage.com/u/mike-gassaway/) | Karate / martial arts |

### Processing Pipeline

1. **Capture**: MOVIN TRACIN records performer motion as BVH + FBX
2. **Retarget**: [movin_sdk_python](https://github.com/MOVIN3D/movin_sdk_python) maps human skeleton to G1 joint limits (1.75m human height)
3. **Ground calibration**: MuJoCo forward kinematics finds minimum foot Z, shifts root for ground contact
4. **Training data**: MuJoCo computes full-body forward kinematics (positions, orientations, velocities)
5. **RL training**: PPO with motion imitation rewards in MuJoCo-Warp (4096 parallel envs)

## Metadata Files

| File | Description |
|------|-------------|
| `manifest.json` | Machine-readable index of all 59 clips with per-clip metadata |
| `quality_report.json` | Automated validation (joint limits, ground penetration, frame consistency) |
| `generate_metadata.py` | Script to regenerate all metadata from source data |

## Citation

```bibtex
@misc{g1moves2026,
  title={G1 Moves: Motion Capture Dataset for the Unitree G1 Humanoid Robot},
  author={Chaiet, Mitch},
  year={2026},
  publisher={GitHub},
  url={https://github.com/experientialtech/g1-moves}
}
```

## License

CC-BY-4.0

## Acknowledgements

- [MOVIN3D](https://movin3d.com/) for the MOVIN TRACIN capture system and movin_sdk_python retargeting SDK
- [Dell Technologies](https://www.dell.com/) for the Pro Max Tower T2 workstation used for capture and training
- [Unitree Robotics](https://www.unitree.com/) for the G1 humanoid robot platform