EgoEsportsQA: An Egocentric Video Benchmark for Perception and Reasoning in Esports
Jianzhe Ma, Zhonghao Cao, Shangkui Chen, Yichen Xu, Wenxuan Wang, Qin Jin

TL;DR
EgoEsportsQA introduces a new benchmark dataset for evaluating perception and reasoning in egocentric esports videos, highlighting current model limitations in high-velocity virtual environments.
Contribution
The paper presents a novel QA benchmark with 1,745 questions from professional esports matches, structured into a taxonomy to evaluate perception and reasoning capabilities of Video-LLMs.
Findings
Current Video-LLMs achieve only 71.58% accuracy on the benchmark.
Models perform better in perception than in tactical reasoning.
Deep micro-operations remain challenging for existing models.
Abstract
While video large language models (Video-LLMs) excel in understanding slow-paced, real-world egocentric videos, their capabilities in high-velocity, information-dense virtual environments remain under-explored. Existing benchmarks focus on daily activities, yet lack a rigorous testbed for evaluating fast, rule-bound reasoning in virtual scenarios. To fill this gap, we introduce EgoEsportsQA, a pioneering video question-answering (QA) benchmark for grounding perception and reasoning in expert esports knowledge. We curate 1,745 high-quality QA pairs from professional matches across 3 first-person shooter games via a scalable six-stage pipeline. These questions are structured into a two-dimensional decoupled taxonomy: 11 sub-tasks in the cognitive capability dimension (covering perception and reasoning levels) and 6 sub-tasks in the esports knowledge dimension. Comprehensive evaluations of…
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