EgoExo-Con: Exploring View-Invariant Video Temporal Understanding

Do Video-LLMs have consistent temporal understandingwhen videos capture the same event from different viewpoints? To studythis question, we introduce EgoExo-Con(sistency), a benchmark of syn-chronized egocentric and exocentric video pairs with human-refinedqueries that ensure all concepts are visible in both viewpoints. EgoExo-Con emphasizes two temporal understanding tasks: Temporal Verificationand Temporal Grounding. It evaluates not only correctness but consis-tency across viewpoints. Our analysis reveals two critical limitations ofexisting Video-LLMs: (1) models often fail to maintain consistency, withresults far worse than their single-view performances. (2) When naivelyfinetuned with synchronized videos of both viewpoints, the models showimproved consistency but often underperform those trained on a singleview. For improvements, we propose View-GRPO, a novel reinforcementlearning framework that effectively strengthens view-specific temporalreasoning while encouraging consistent comprehension across viewpoints.Our method demonstrates its superior temporal understanding capabili-ties, especially for improving cross-view consistency. All resources havebeen made available at EgoExo-Con.