Vision-based beatmap extraction in rhythm game toward platform-aware note generation

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 133
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorKIM, YEONGHUNko
dc.contributor.authorChoi, Sungheeko
dc.date.accessioned2022-01-21T06:51:01Z-
dc.date.available2022-01-21T06:51:01Z-
dc.date.created2021-12-27-
dc.date.created2021-12-27-
dc.date.issued2021-08-18-
dc.identifier.citationIEEE Conference on Games (IEEE CoG), pp.901 - 905-
dc.identifier.urihttp://hdl.handle.net/10203/291980-
dc.description.abstractRecent approaches to deep learning-based music analysis have had significant impact on procedural content generation in music-based games. However, the lack of understanding of the unique features of various platforms and interfaces makes auto-generated content less valuable than manually designed content. Hand-crafted datasets are required, to enhance the quality of content in various platforms, but most rhythm games permit only indirect access to the dataset, as a form of player's experience and its replay video. We develop a vision-based approach to content extraction through video analysis, using a format named beatmap. We cover some common visualized features in well-known rhythm games, and construct a mapping from their content to our beatmap model, using multiple object detection. Our method correctly detects each action button, type, and time, and extracts beatmap representations for our target game.-
dc.languageEnglish-
dc.publisherIEEE-
dc.titleVision-based beatmap extraction in rhythm game toward platform-aware note generation-
dc.typeConference-
dc.identifier.wosid000842962500121-
dc.identifier.scopusid2-s2.0-85122951604-
dc.type.rimsCONF-
dc.citation.beginningpage901-
dc.citation.endingpage905-
dc.citation.publicationnameIEEE Conference on Games (IEEE CoG)-
dc.identifier.conferencecountryDK-
dc.identifier.conferencelocationVirtual-
dc.identifier.doi10.1109/CoG52621.2021.9619108-
dc.contributor.localauthorChoi, Sunghee-
Appears in Collection
CS-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0