GameVibe: A Multimodal Affective Game Corpus
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915219986448384 |
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| author | Barthet, Matthew Kaselimi, Maria Pinitas, Kosmas Makantasis, Konstantinos Liapis, Antonios Yannakakis, Georgios N. |
| author_facet | Barthet, Matthew Kaselimi, Maria Pinitas, Kosmas Makantasis, Konstantinos Liapis, Antonios Yannakakis, Georgios N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12787 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GameVibe: A Multimodal Affective Game Corpus Barthet, Matthew Kaselimi, Maria Pinitas, Kosmas Makantasis, Konstantinos Liapis, Antonios Yannakakis, Georgios N. Human-Computer Interaction Artificial Intelligence 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. |
| title | GameVibe: A Multimodal Affective Game Corpus |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2407.12787 |