Beyond the Single Camera: Agentic Multi-View Reasoning in Sports Video Understanding
Recent Multimodal Large Language Models (MLLMs) achieve strong performance on single-view video understanding benchmarks. However, sports videos involve dense occlusion, rapid motion, and complex interactions that are difficult to resolve from a single viewpoint. In practice, sports events are recorded from multiple camera angles, providing complementary evidence used by referees. Yet, no existing benchmark evaluates MLLMs on multi-view sports video understanding. To address this gap, we introduce SportMV-Bench, a comprehensive benchmark built from official match recordings, through a dedicate
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- LinkedLinked via arxiv author · 85%Kerui Chen →
“Beyond the Single Camera: Agentic Multi-View Reasoning in Sports Video Understanding”
- LinkedLinked via arxiv author · 85%Jinglu Wang →
“Beyond the Single Camera: Agentic Multi-View Reasoning in Sports Video Understanding”
- LinkedLinked via arxiv author · 85%Xiaoyi Zhang →
“Beyond the Single Camera: Agentic Multi-View Reasoning in Sports Video Understanding”
- LinkedLinked via arxiv author · 85%Yan Lu →
“Beyond the Single Camera: Agentic Multi-View Reasoning in Sports Video Understanding”