---
pretty_name: PointMotionBench
---

# PointMotionBench

A benchmark for evaluating 3D point motion in video, covering egocentric and third-person scenes across three source datasets. Each sample pairs an RGB video clip with per-object 3D and 2D tracked surface points and a human-verified natural-language caption.

## Overview

| Dataset | Clips | Video format | Tracks | Scene type |
|---------|-------|--------------|--------|------------|
| DAVIS | 90 | mp4, 24 fps | 2D + 3D | Third-person, diverse outdoor/indoor |
| HOT3D | 2,475 | mp4, 30 fps | 2D + 3D | Egocentric, object manipulation (Aria) |
| WorldTrack | 155 | npz (frames embedded), 30 fps | 3D (+2D) | Egocentric + studio, 4 splits |

---

## Setup

### Step 1 — Download PointMotionBench

Benchmark data provided in this repository include annotations, captions, indices, and scripts created by Ai2 that correspond to the source datasets.


```python
# pip install huggingface_hub
# If the download stalls near completion: HF_HUB_DISABLE_XET=1 python your_script.py
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id="allenai/PointMotionBench",
    repo_type="dataset",
    local_dir=".",
)
```

---

### Step 2 — HOT3D: Download Videos

We do not share videos from HOT3D. Users should access the videos from the source dataset at [bop-benchmark/hot3d](https://huggingface.co/datasets/bop-benchmark/hot3d) (HuggingFace).

**Requirements:** `imageio[ffmpeg]`, `imageio-ffmpeg`, `opencv-python-headless`, `numpy`

```bash
# 1. download train_aria TARs (~1,516 clips)
#    to download only the 1,272 clips needed for PointMotionBench, add:
#    --captions hot3d/hot3d_annotations.json
python hot3d/download_train_aria.py --output /path/to/train_aria

# 2. extract undistorted upright RGB videos (one mp4 per TAR)
python hot3d/extract_rgbs.py \
    --clips_dir  /path/to/train_aria \
    --output_dir /path/to/rgbs

# 3. trim to PointMotionBench windows
python hot3d/trim_hot3d_clips.py \
    --src_dir    /path/to/rgbs \
    --captions   hot3d/hot3d_annotations.json \
    --output_dir hot3d/videos
```

For large-scale extraction, `extract_rgbs.py` supports sharding:

```bash
python hot3d/extract_rgbs.py \
    --clips_dir  /path/to/train_aria \
    --output_dir /path/to/rgbs \
    --shard_idx  0 \
    --num_shards 8
```

---

### Step 3 — WorldTrack: extract clips

Download the WorldTrack source data (WorldTrack benchmark, introduced in St4RTrack, Feng et al., ICCV 2025 — dataset download available at [HavenFeng/St4RTrack](https://github.com/HavenFeng/St4RTrack)). The source data should have this layout:

```
WorldTrack/
├── adt_mini/        # Aria Digital Twin
├── ds_mini/         # Dynamic Scenes
├── po_mini/         # POtential Objects
└── pstudio_mini/    # PStudio
```

Then extract PointMotionBench clips using the index map from Step 1:

```bash
python worldtrack/extract_worldtrack_clips.py \
    --index_map  worldtrack/worldtrack_index_map.json \
    --src_dir    /path/to/WorldTrack \
    --output_dir worldtrack
```

| Split | Clips | Frames per clip | Scene type |
|-------|-------|-----------------|------------|
| `adt_mini` | 39 | 12–300 | Apartment indoor, egocentric (Aria Digital Twin) |
| `ds_mini` | 52 | 39–128 | Dynamic indoor scenes |
| `po_mini` | 16 | 78–128 | Mixed indoor (cab, seminar, egobody) |
| `pstudio_mini` | 48 | 150 | Studio sports (basketball, football, tennis, etc.) |

## Intended Use

PointMotionBench is provided for benchmarking purposes. 
It intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use).

## Disclaimer 
PointMotionBench data maps to the videos and other source data that are not shared in this repository. 
Such videos and data are provided by the owners of the source datasets above, and remain subject to their respective 
license terms and use restrictions. Users who access videos and data from these sources are responsible for 
reviewing and confirming that their use complies with the terms and conditions.