metadata
license: other
license_name: research-only
task_categories:
- video-classification
- feature-extraction
tags:
- video
- motion
- similarity
- retrieval
- benchmark
language:
- en
pretty_name: SimMotion-Real
size_categories:
- n<1K
SimMotion-Real Benchmark
Real-world benchmark for evaluating motion representation consistency, introduced in:
"SemanticMoments: Training-Free Motion Similarity via Third Moment Features" (arXiv:2602.09146)
License: For research purposes only.
Dataset Description
The benchmark consists of 40 real-world test cases, each organized as a triplet:
| File | Description |
|---|---|
ref.mp4 |
Reference video defining the target semantic motion |
positive.mp4 |
Video sharing the same semantic motion as reference |
negative.mp4 |
Hard negative - similar appearance but different motion |
Usage
from semantic_moments import SimMotionReal, download_simmotion
# Download
download_simmotion(dataset="real")
# Load
dataset = SimMotionReal("SimMotion_Real_benchmark")
print(f"Loaded {len(dataset)} triplets")
for triplet in dataset:
print(triplet.ref_path, triplet.positive_path, triplet.negative_path)
Or download directly:
huggingface-cli download Shuberman/SimMotion-Real --repo-type dataset --local-dir SimMotion_Real_benchmark
Evaluation Protocol
- Retrieval Pool: For each reference, candidates include the positive, hard negative, and 1,000 Kinetics-400 distractors
- Metric: Top-1 Accuracy - success if positive is retrieved first
Citation
@article{huberman2026semanticmoments,
title={SemanticMoments: Training-Free Motion Similarity via Third Moment Features},
author={Huberman, Saar and Goldberg, Kfir and Patashnik, Or and Benaim, Sagie and Mokady, Ron},
journal={arXiv preprint arXiv:2602.09146},
year={2026}
}
License
For research purposes only.