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ARXIV:2603.18856 · VIDEO REASONING · SUBMITTED 20 MAR · 21:29 UTC · FRESHNESS STALE
ARXIV:2603.18856VIDEO REASONINGSUBMITTED 20 MAR · 21:29 UTCFRESHNESS STALEBishoy Galoaa · Shayda Moezzi · Xiangyu Bai · Sarah Ostadabbas · arXiv
A motion-centric video understanding model that makes object trajectories explicit and verifiable, improving spatial-temporal grounding and trajectory prediction.
Opportunity summary
Pain A motion-centric video understanding model that makes object trajectories explicit and verifiable, improving spatial-temporal grounding and trajectory prediction.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence partial
A motion-centric video understanding model that makes object trajectories explicit and verifiable, improving spatial-temporal grounding and trajectory prediction. At the same time, a growing set of datasets and benchmarks now provides structured annotations designed…
Recent research has made substantial progress on video reasoning, with many models leveraging spatio-temporal evidence chains to strengthen their inference capabilities. At the same time, a growing set of datasets and benchmarks now provides…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. At the same time, a growing set of datasets and benchmarks now provides structured annotations designed to support and evaluate such reasoning. A public…
Video Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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A motion-centric video understanding model that makes object trajectories explicit and verifiable, improving spatial-temporal grounding and trajectory prediction.
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10.48550/arXiv.2603.18856A motion-centric video understanding model that makes object trajectories explicit and verifiable, improving spatial-temporal grounding and trajectory prediction.
Abstract
Recent research has made substantial progress on video reasoning, with many models leveraging spatio-temporal evidence chains to strengthen their inference capabilities. At the same time, a growing set of datasets and benchmarks now provides structured annotations designed to support and evaluate such reasoning. However, little attention has been paid to reasoning about \emph{how} objects move between observations: no prior work has articulated the motion patterns by connecting successive observations, leaving trajectory understanding implicit and difficult to verify. We formalize this missing capability as Spatial-Temporal-Trajectory (STT) reasoning and introduce \textbf{Motion-o}, a motion-centric video understanding extension to visual language models that makes trajectories explicit and verifiable. To enable motion reasoning, we also introduce a trajectory-grounding dataset artifact that expands sparse keyframe supervision via augmentation to yield denser bounding box tracks and a stronger trajectory-level training signal. Finally, we introduce Motion Chain of Thought (MCoT), a structured reasoning pathway that makes object trajectories through discrete \texttt{<motion/>} tag summarizing per-object direction, speed, and scale (of velocity) change to explicitly connect grounded observations into trajectories. To train Motion-o, we design a reward function that compels the model to reason directly over visual evidence, all while requiring no architectural modifications. Empirical results demonstrate that Motion-o improves spatial-temporal grounding and trajectory prediction while remaining fully compatible with existing frameworks, establishing motion reasoning as a critical extension for evidence-based video understanding. Code is available at https://github.com/ostadabbas/Motion-o.
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partial0 refs; 0 sources; 50% coverage.
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Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
A motion-centric video understanding model that makes object trajectories explicit and verifiable, improving spatial-temporal grounding and trajectory prediction. At the same time, a growing set of datasets and benchmarks now provides structured annotations designed to support a...
METHOD
Recent research has made substantial progress on video reasoning, with many models leveraging spatio-temporal evidence chains to strengthen their inference capabilities. At the same time, a growing set of datasets and benchmarks now provides structured annotations designed to su...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. At the same time, a growing set of datasets and benchmarks now provides structured annotations designed to support and evaluate such reasoning. A public repository is linked, so build verification can ins...
WHY NOW
Video Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A motion-centric video understanding model that makes object trajectories explicit and verifiable, improving spatial-temporal grounding and trajectory prediction. At the same time, a growing set of datasets and benchmarks now provides structured annotations designed to support and evaluate such reasoning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Recent research has made substantial progress on video reasoning, with many models leveraging spatio-temporal evidence chains to strengthen their inference capabilities. At the same time, a growing set of datasets and benchmarks now provides structured annotations designed to support and evaluate such reasoning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. At the same time, a growing set of datasets and benchmarks now provides structured annotations designed to support and evaluate such reasoning. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Video Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A motion-centric video understanding model that makes object trajectories explicit and verifiable, improving spatial-temporal grounding and trajectory prediction.
Segment
Video Reasoning
Adoption evidence
Public code linked for build inspection
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reason
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Technical feasibility
partial
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