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README.md

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+# SimMotion-Real Benchmark (SemanticMoments)
+
+This folder contains the real-world benchmark for evaluating motion representation consistency as described in:
+
+**"SemanticMoments: Training-Free Motion Similarity via Third Moment Features"**
+
+## Dataset Structure
+The benchmark consists of **40 real-world test cases**, each organized as a triplet:
+1. **`ref.mp4`**: The reference video defining the target semantic motion.
+2. **`positive.mp4`**: A real-world video sharing the same semantic motion as the reference.
+3. **`negative.mp4`**: **Hard Negative**. A video with a similar visual appearance to the reference, but containing a different motion.
+
+## Evaluation Protocol
+This benchmark evaluates retrieval performance using a large-scale distractor pool to ensure motion representations are robust against diverse real-world actions.
+
+* **Retrieval Pool**: For each reference video, the candidate pool includes:
+    * The corresponding `positive.mp4`.
+    * The corresponding `hard negative.mp4`.
+    * **1,000 Distractor Videos**: The first 1,000 videos from the Kinetics-400 validation set (sorted alphabetically).
+* **Metric**: Top-1 Accuracy. A trial is successful if:
+$$Similarity(ref, positive) > Similarity(ref, candidates_{all})$$
+(Where $candidates_{all}$ includes the hard negative and all 1,000 Kinetics distractors).
+
+## Citation
+If you use this benchmark, please cite:
+
+```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}
+}
+```

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