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 traces 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.
View on arXiv@article{barthet2025_2407.12787,
title={ GameVibe: A Multimodal Affective Game Corpus },
author={ Matthew Barthet and Maria Kaselimi and Kosmas Pinitas and Konstantinos Makantasis and Antonios Liapis and Georgios N. Yannakakis },
journal={arXiv preprint arXiv:2407.12787},
year={ 2025 }
}