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We consider the problem of automatic highlight-detection in video gamestreams. Currently, the vast majority of highlight-detection systems for gamesare triggered by the occurrence of hard-coded game events (e.g., score change,end-game), while most advanced tools and techniques are based on detection ofhighlights via visual analysis of game footage. We argue that in the context ofgame streaming, events that may constitute highlights are not only dependent ongame footage, but also on social signals that are conveyed by the streamerduring the play session (e.g., when interacting with viewers, or whencommenting and reacting to the game). In this light, we present a multi-viewunsupervised deep learning methodology for novelty-based highlight detection.The method jointly analyses both game footage and social signals such as theplayers facial expressions and speech, and shows promising results forgenerating highlights on streams of popular games such as Player Unknown'sBattlegrounds.

Deep Unsupervised Multi-View Detection of Video Game Stream Highlights