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.