Datasets:

allenai
/

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.

# 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 (HuggingFace).

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

# 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:

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). 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:

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.

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.