  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.
