README.md

---
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](https://arxiv.org/abs/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

```python
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:
```bash
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

```bibtex
@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.