  We consider the problem of automatic highlight-detection in video game
streams. Currently, the vast majority of highlight-detection systems for games
are 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 of
highlights via visual analysis of game footage. We argue that in the context of
game streaming, events that may constitute highlights are not only dependent on
game footage, but also on social signals that are conveyed by the streamer
during the play session (e.g., when interacting with viewers, or when
commenting and reacting to the game). In this light, we present a multi-view
unsupervised deep learning methodology for novelty-based highlight detection.
The method jointly analyses both game footage and social signals such as the
players facial expressions and speech, and shows promising results for
generating highlights on streams of popular games such as Player Unknown's
Battlegrounds.
