Game-MUG: Multimodal Oriented Game Situation Understanding and Commentary Generation Dataset

arXiv:2404.19175v1 Announce Type: new
Abstract: The dynamic nature of esports makes the situation relatively complicated for average viewers. Esports broadcasting involves game expert casters, but the caster-dependent game commentary is not enough to fully understand the game situation. It will be richer by including diverse multimodal esports information, including audiences' talks/emotions, game audio, and game match event information. This paper introduces GAME-MUG, a new multimodal game situation understanding and audience-engaged commentary generation dataset and its strong baseline. Our dataset is …

abstract arxiv broadcasting commentary cs.cl dataset diverse dynamic esports expert game information multimodal nature type understanding will