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This paper introduces a large scale multimodal corpus collected for thepurpose of analysing and predicting player engagement in commercial-standardgames. The corpus is solicited from 25 players of the action role-playing gameTom Clancy's The Division 2, who annotated their level of engagement using atime-continuous annotation tool. The cleaned and processed corpus presented inthis paper consists of nearly 20 hours of annotated gameplay videos accompaniedby logged gamepad actions. We report preliminary results on predictinglong-term player engagement based on in-game footage and game controlleractions using Convolutional Neural Network architectures. Results obtainedsuggest we can predict the player engagement with up to 72% accuracy on average(88% at best) when we fuse information from the game footage and the player'scontroller input. Our findings validate the hypothesis that long-term (i.e. 1hour of play) engagement can be predicted efficiently solely from pixels andgamepad actions.

Predicting Player Engagement in Tom Clancy's The Division 2: A Multimodal Approach via Pixels and Gamepad Actions