README.md

---
language:
- en
license: apache-2.0
task_categories:
- robotics
- reinforcement-learning
pretty_name: HumanTracker
size_categories:
- 1K<n<10K
tags:
- humanoid
- motion-tracking
- mocap
- preference
- reward-model
configs:
- config_name: preference
  data_files:
  - split: train
    path: preference_pair/train/train-*.parquet
  - split: test
    path: preference_pair/test/test-*.parquet
---

# Dataset Card for HumanTracker

[Project page](https://dairuliu.github.io/humantracker/) · [Paper](https://arxiv.org/abs/2608.13555) · [Code](https://github.com/GalaxyGeneralRobotics/HumanTracker)

HumanTracker is a humanoid motion-tracking benchmark. This release contains two complementary subsets:

- **`motions/`** — the evaluation test split: retargeted 29-DoF reference trajectories, grouped into four motion families.
- **`preference_pair/`** — 6,000 human preference pairs, each stored with the two tracker rollouts that were compared and the source-motion clip they track.

The evaluation harness and HumanScore reward model live in the [HumanTracker repository](https://github.com/GalaxyGeneralRobotics/HumanTracker). `preference_pair/` is the reward model's training input as published: the rollouts are inline, so nothing has to be re-simulated to reproduce HumanScore.

## Dataset Details

Humanoid tracking is often scored with per-frame kinematic error, which misses the physical artifacts people notice in video — unstable support, foot skating, mistimed contacts. HumanTracker pairs a large, family-labeled motion test set with a preference-aligned metric (HumanScore) trained on pairwise human comparisons.

| Subset | Role | Size |
| --- | --- | --- |
| `motions/` | Tracker evaluation references (test split) | 2,500 clips |
| `preference_pair/` | Human preference labels + the compared tracker rollouts | 6,000 pairs (4,800 / 1,200), 10 GB |

Motions are retargeted to a 29-DoF Unitree G1-style humanoid with [GMR](https://arxiv.org/abs/2510.02252) and stored as `qpos` trajectories at 50 Hz. Preference pairs compare GMT, TWIST2, SONIC and Humanoid-GPT rollouts of the same reference window (typically 250 frames / 5 s). Labels are a strict preference, `similar`, or `bad_traj` (cannot compare). The pair split is grouped by `motion_id`, so every clip from one source motion stays in one partition.

**Paper:** [HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark](https://arxiv.org/abs/2608.13555) (ECCV 2026).

**License:** Apache 2.0.

## Dataset Structure

```
HumanTracker/
  README.md
  motions/
    test.json
    Daily/
    Ground/
    HighlyDynamic/
    Interaction/
  preference_pair/
    train.json
    test.json
    train/train-00000-of-00020.parquet ... train-00019-of-00020.parquet
    test/test-00000-of-00005.parquet  ... test-00004-of-00005.parquet
```

Filenames are anonymized for release. Dates, performer names, capture-system tags and sample-rate suffixes are removed. Family-level names (`Daily`, `Interaction`, `HighlyDynamic`) are numbered (`Daily_1.npz`). Action labels that are themselves the motion type are kept: Ground actions such as `burpee` and `sit-lie`, and Highly Dynamic actions such as `Tennis` or named martial-arts skills.

### Motions (`motions/`)

`motions/test.json` is a list of

```json
{"path": "Daily/Daily_1.npz", "category": "Daily", "frames": 1584}
```

| Family | Test clips | What it stresses |
| --- | --- | --- |
| Daily | 974 | steady locomotion, mild contacts |
| Interaction | 1,094 | hands–body coordination |
| HighlyDynamic | 268 | impacts, aerial phases, fast footwork |
| Ground | 164 | low posture, multi-contact transitions |
| **Total** | **2,500** | |

Each `.npz` contains:

| Key | Shape | Description |
| --- | --- | --- |
| `qpos` | `(T, 36)` | generalized positions (floating base + 29 DoF) |
| `qvel` | `(T, 35)` | generalized velocities |
| `kpt2gv_pose` | `(T, 14, 4, 4)` | 14 keypoint poses in the gravity-aligned frame |
| `kpt_cvel_in_gv` | `(T, 14, 6)` | keypoint spatial velocities |
| `gv_vel` | `(T, 3)` | root linear velocity in the gravity-aligned frame |
| `gv2wrd_pose` | `(T, 4, 4)` | gravity-aligned frame to world |
| `foot_contact` | `(T, 2)` | left / right foot contact |

The evaluator in the code repository reads the same manifest:

```python
from pathlib import Path
import json
import numpy as np

root = Path("motions")
items = json.loads((root / "test.json").read_text())
item = items[0]
traj = np.load(root / item["path"])
qpos = traj["qpos"]          # (frames, 36)
category = item["category"]  # Daily | Ground | HighlyDynamic | Interaction
```

```bash
python -m humantracker.eval.eval_parallel_tracker \
    --tracker sonic \
    --mocap_path /path/to/HumanTracker/motions \
    --test_json /path/to/HumanTracker/motions/test.json \
    --termination_metric whole_body
```

`path` is relative to `motions/`. The first path component must match `category`.

### Preference pairs (`preference_pair/`)

Load with 🤗 Datasets:

```python
from datasets import load_dataset

ds = load_dataset("GalaxyGeneralRobotics/HumanTracker", name="preference")
row = ds["train"][0]
print(row["choice_type"], row["tracker_pair_key"], row["motion_id"])
```

Or read the parquet shards directly, which is what the reward-model trainer does:

```python
import io
import json
import numpy as np
import pyarrow.parquet as pq

table = pq.read_table("preference_pair/test/test-00000-of-00005.parquet")
row = table.slice(0, 1).to_pylist()[0]

annotation = json.loads(row["annotation_json"])
reference = np.load(io.BytesIO(row["motion_npz"]))        # same keys as motions/*.npz
candidate_0 = np.load(io.BytesIO(row["candidate_0_npz"]))
candidate_1 = np.load(io.BytesIO(row["candidate_1_npz"]))
print(row["choice_type"], row["preferred_candidate_idx"], row["candidate_0_tracker"])
print(candidate_0["joint_pos"].shape)                     # (num_frames, 29)
```

| Column | Description |
| --- | --- |
| `record_id` / `pair_id` | anonymous pair id |
| `motion_id` | anonymized source-motion id (`Daily_12`, `burpee_3`, `Tennis_8`, …) |
| `category` | motion family |
| `tracker_pair_key` | unordered tracker pair, e.g. `gmt\|twist2` |
| `candidate_0_tracker` / `candidate_1_tracker` | which tracker occupies each candidate slot |
| `choice_type` | `preference` / `similar` / `bad_traj` |
| `preferred_candidate_idx` | `0` or `1` when `choice_type == preference`, else null |
| `source_start_frame` / `source_end_frame` | clip range in the original capture |
| `num_frames` / `fps` | clip length and 50 Hz |
| `candidate_0_npz` / `candidate_1_npz` | the two tracker rollouts (bytes, `np.savez_compressed`) |
| `motion_npz` | source-motion clip (bytes, `np.savez_compressed`) |
| `annotation_json` | full cleaned record (candidates, preference, flags, annotator alias) |

Candidate slots are stable identities, not display positions: `preferred_candidate_idx` indexes them, and the order the annotator saw is recorded separately in `annotation_json`. `motion_npz` carries the same keys as `motions/*.npz`, already sliced to `[source_start_frame, source_end_frame)`. Most windows are 250 frames (5 s at 50 Hz); shorter tail windows are kept and right-padded at training time.

Each candidate NPZ is one tracker's closed-loop rollout of that window, frame-aligned with the reference, `float32`, `num_frames` rows per array:

| Block | Arrays | Dims |
| --- | --- | --- |
| Reference the tracker was following | `ref_pose`, `ref_root_navi_vel`, `ref_joint_pos`, `ref_joint_vel`, `ref_foot_contact` | 70 |
| Simulated rollout | `sensor_pose`, `imu_pose`, `action`, `motor_target`, `joint_pos`, `joint_vel`, `foot_contact`, `foot_force`, `foot_vel`, `foot_acc`, `linvel_pelvis`, `root_navi_vel`, `acu_root2gv_lin_vel`, `acu_root2gv_ang_vel`, `acu_kpt2gv_pose`, `acu_kpt_cvel_in_gv` | 469 |
| Future-reference residuals | `next_ref2acu_gv_vel`, `next_ref2acu_kpt_pose`, `next_ref2acu_kpt_cvel` | 311 |
| Rendering | `qpos`, `qvel` | 71 |

The reported HumanScore model concatenates the first two blocks into a 539-d per-frame token; the residual block is shipped for the paper's appendix ablation and is unused by default. `qpos` / `qvel` are the MuJoCo generalized state, for replaying a rollout in the viewer.

`train.json` and `test.json` list the `record_id`s of each split, grouped by `motion_id`. `train.json` also carries `model_selection`, the 461 records held out for epoch selection, so a rerun selects the same checkpoint as the published one. `bad_traj` pairs are excluded from the fit, leaving 5,757 trained pairs; `preference` uses a Bradley–Terry loss and `similar` a symmetric 0.5 target.

| Split | Pairs | Source motions | preference / similar / bad_traj |
| --- | --- | --- | --- |
| train | 4,800 | 4,486 | 3,850 / 759 / 191 |
| test | 1,200 | 812 | 958 / 190 / 52 |
| **total** | **6,000** | **5,298** | **4,808 / 949 / 243** |

The six unordered tracker pairs (`gmt|hgpt`, `gmt|sonic`, `gmt|twist2`, `hgpt|sonic`, `hgpt|twist2`, `sonic|twist2`) are balanced at 1,000 pairs each.

Training HumanScore from this directory:

```bash
python -m humantracker.reward_model.train.trainer \
    --data_dir /path/to/HumanTracker/preference_pair \
    --cache_dir /path/to/feature_cache \
    --output_dir storage/checkpoints/reward_model
```

## Uses

- **Tracker evaluation.** Run a policy on `motions/` with the published evaluator and report Succ / MPJPE / HumanScore per family.
- **Reward-model / HumanScore research.** Reproduce or extend HumanScore directly from `preference_pair/`; the [code repository](https://github.com/GalaxyGeneralRobotics/HumanTracker) reads this directory as its `--data_dir`.
- **Diagnostics.** Family labels and retained action names (`burpee`, `Tennis`, …) support fine-grained error breakdowns.

This release is **not** a full training-motion dump. The 2,500 evaluation clips are the official test split; preference clips are the labeled 5 s windows, not the complete source takes.

## Citation

```bibtex
@misc{liu2026humantrackercomprehensivehumanalignedmotion,
      title={HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark},
      author={Dairu Liu and Zekun Qi and Jiayu Zeng and Ruixi Yu and Yu Guan and Yintianrun Zhang and Xuchuan Chen and Sikai Liang and Zekai Li and Chenghuai Lin and Xinqiang Yu and Wenyao Zhang and He Wang and Li Yi},
      year={2026},
      eprint={2608.13555},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2608.13555},
}
```