SemanticMoments: Training-Free Motion Similarity via Third Moment Features

Saar Huberman, Kfir Goldberg, Or Patashnik, Sagie Benaim, Ron Mokady

SemanticMoments: Training-Free Motion Similarity via Third Moment Features: 22 upvotes on Hugging Face Daily Papers, #7 of 32 papers on 2026-02-16. Day-by-day upvote history.

Retrieving videos based on semantic motion is a fundamental, yet unsolved, problem. Existing video representation approaches overly rely on static appearance and scene context rather than motion dynamics, a bias inherited from their training data and objectives. Conversely, traditional motion-centric inputs like optical flow lack the semantic grounding needed to understand high-level motion. To demonstrate this inherent bias, we introduce the SimMotion benchmarks, combining controlled synthetic data with a new human-annotated real-world dataset. We show that existing models perform poorly on these benchmarks, often failing to disentangle motion from appearance. To address this gap, we propose SemanticMoments, a simple, training-free method that computes temporal statistics (specifically, higher-order moments) over features from pre-trained semantic models. Across our benchmarks, SemanticMoments consistently outperforms existing RGB, flow, and text-supervised methods. This demonstrates that temporal statistics in a semantic feature space provide a scalable and perceptually grounded foundation for motion-centric video understanding.

Paper page on Hugging Face · arXiv

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