GameVibe: A Multimodal Affective Game Corpus
arXiv:2407.12787 · doi:10.1038/s41597-024-04022-4
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.
12 pages, 5 figures, 1 table
References in corpus (7)
- AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
- Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
- SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild
- K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations
- The Arousal video Game AnnotatIoN (AGAIN) Dataset
- Predicting Player Engagement in Tom Clancy's The Division 2: A Multimodal Approach via Pixels and Gamepad Actions
- Across-Game Engagement Modelling via Few-Shot Learning