GameVibe: A Multimodal Affective Game Corpus
Matthew Barthet, Maria Kaselimi, Kosmas Pinitas, Konstantinos, Makantasis, Antonios Liapis, Georgios N. Yannakakis

TL;DR
GameVibe is a new multimodal affective game corpus with diverse gameplay videos and affect annotations, designed to support affective computing research involving audiovisual stimuli and viewer engagement analysis.
Contribution
It introduces a high-quality, diverse multimodal dataset of gameplay videos with affect annotations, filling a gap in available resources for affective computing research.
Findings
High inter-annotator agreement demonstrated reliability
Diverse set of 30 games enhances generalizability
Rich multimodal data supports advanced affective analysis
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsArtificial Intelligence in Games · Digital Games and Media
MethodsSoftmax · Attention Is All You Need · Sparse Evolutionary Training
