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New MotionBlind benchmark reveals Video-LLMs struggle with motion understanding

By PulseAugur Editorial · [1 sources] ·

A new benchmark called MotionBlind has been developed to test the motion understanding capabilities of Video Large Language Models (Video-LLMs). Researchers found that most open-source Video-LLMs perform poorly, often failing to distinguish between different speeds or directions of motion, even when presented with clear visual data. While Gemini-3.1 Pro showed some improvement, it still struggled with accurately assessing speed, indicating that current Video-LLMs are not yet reliable for tasks requiring a true understanding of motion. AI

IMPACT Highlights critical limitations in current Video-LLMs, suggesting they are not yet suitable for applications requiring nuanced motion perception.

RANK_REASON Research paper introducing a new benchmark for evaluating Video-LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MotionBlind benchmark reveals Video-LLMs struggle with motion understanding

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Research paper introducing a new benchmark for evaluating Video-LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Dhairya Bhatia, Bishoy Galoaa, Oliver Fritsche, Shahid Kamal, Muhammad Obaidullah Abdul Salam, Umer Saleem, Om Rastogi, Frania Felix Chettiar, Nesli Erdogmus, Sarah Ostadabbas ·

    MotionBlind: Probing the Illusion of Motion Understanding in Video-LLMs

    arXiv:2609.09528v1 Announce Type: new Abstract: Video large language models (Video-LLMs) are increasingly used as the perceptual front end of world models, a role that assumes they can read motion: how fast something moves, which way it travels, how hard it is pushed. We show the…

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