metadata
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 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 |
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)
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)
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
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 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.
Performers
| Prefix | Performer | Clips |
|---|---|---|
B_ |
Mitch Chaiet | Bonus clips + some dance |
J_ |
Jasmine Coro | Dance choreography |
M_ |
Mike Gassaway | Karate / martial arts |
Processing Pipeline
- Capture: MOVIN TRACIN records performer motion as BVH + FBX
- Retarget: movin_sdk_python maps human skeleton to G1 joint limits (1.75m human height)
- Ground calibration: MuJoCo forward kinematics finds minimum foot Z, shifts root for ground contact
- Training data: MuJoCo computes full-body forward kinematics (positions, orientations, velocities)
- 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
@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 for the MOVIN TRACIN capture system and movin_sdk_python retargeting SDK
- Dell Technologies for the Pro Max Tower T2 workstation used for capture and training
- Unitree Robotics for the G1 humanoid robot platform