Paper ID: 2407.12787

GameVibe: A Multimodal Affective Game Corpus

Matthew Barthet, Maria Kaselimi, Kosmas Pinitas, Konstantinos Makantasis, Antonios Liapis, Georgios N. Yannakakis

As online video and streaming platforms continue to grow, affective computing research has undergone a shift towards more complex studies involving multiple modalities. However, there is still a lack of readily available datasets with high-quality audiovisual stimuli. In this paper, we present GameVibe, a novel affect corpus which consists of multimodal audiovisual stimuli, including in-game behavioural observations and third-person affect labels for viewer engagement. The corpus consists of videos from a diverse set of publicly available gameplay sessions across 30 games, with particular attention to ensure high-quality stimuli with good audiovisual and gameplay diversity. Furthermore, we present an analysis on the reliability of the annotators in terms of inter-annotator agreement.

Submitted: Jun 17, 2024