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

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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 huggingface-cli download ...
huggingface-cli download allenai/PointMotionBench \
    --repo-type dataset --local-dir $POINTMOTIONBENCH_ROOT

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Step 2 — DAVIS: Download Videos

DAVIS videos should be reconstructed from the source data at the official source. Download Trainval 2017 - Images (480p) and Annotations, then convert frames to mp4 (requires ffmpeg):

python davis/reconstruct_davis.py \
    --davis-root /path/to/DAVIS \
    --output-dir davis/videos/input_480p

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Step 3 — HOT3D: Download Videos

HOT3D videos should be reconstructed from the source data at bop-benchmark/hot3d (HuggingFace).

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

python hot3d/reconstruct_hot3d.py \
    --workdir    /path/to/hot3d_work \
    --output-dir hot3d/rgbs

This runs all three stages (download TARs → extract RGB → trim to PointMotionBench windows). For large-scale extraction, run the three scripts individually — extract_rgbs.py supports sharding:

python hot3d/extract_rgbs.py \
    --clips_dir  /path/to/hot3d_work/train_aria \
    --output_dir /path/to/hot3d_work/rgbs \
    --shard_idx  0 \
    --num_shards 8

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Step 4 — WorldTrack: Reconstruct 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/reconstruct_worldtrack.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.