BEGIN:VCALENDAR VERSION:2.0 PRODID:Linklings LLC BEGIN:VTIMEZONE TZID:Asia/Seoul X-LIC-LOCATION:Asia/Seoul BEGIN:STANDARD TZOFFSETFROM:+0900 TZOFFSETTO:+0900 TZNAME:KST DTSTART:18871231T000000 DTSTART:19881009T020000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20230103T035307Z LOCATION:Auditorium\, Level 5\, West Wing DTSTART;TZID=Asia/Seoul:20221206T100000 DTEND;TZID=Asia/Seoul:20221206T120000 UID:siggraphasia_SIGGRAPH Asia 2022_sess153_papers_565@linklings.com SUMMARY:LayoutEnhancer: Generating Good Indoor Layouts from Imperfect Data DESCRIPTION:Technical Papers\n\nLayoutEnhancer: Generating Good Indoor Lay outs from Imperfect Data\n\nLeimer, Guerrero, Weiss, Musialski\n\nWe addre ss the problem of indoor layout synthesis, which is a topic of continuing research interest in computer graphics. The newest works made significant progress using data-driven generative methods; however, these approaches r ely on suitable datasets. In practice, some desirable layout properties ma y not exist in a dataset, for instance, specific expert knowledge can be m issing in the data.\n\nWe propose a method that combines expert knowledge, for example, knowledge about ergonomics, with a data-driven generator bas ed on the popular Transformer architecture. The knowledge is given as diff erentiable scalar functions, which can be used both as weights or as addit ional terms in the loss function. Using this knowledge, the synthesized la youts can be biased to exhibit desirable properties, even if these propert ies are not present in the dataset.\n\nOur approach can also alleviate pro blems of lack of data and imperfections in the data. Our work aims to impr ove generative machine learning for modeling and provide novel tools for d esigners and amateurs for the problem of interior layout creation.\n\nRegi stration Category: FULL ACCESS, EXPERIENCE PLUS ACCESS, EXPERIENCE ACCESS, TRADE EXHIBITOR\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON URL:https://sa2022.siggraph.org/en/full-program/?id=papers_565&sess=sess15 3 END:VEVENT END:VCALENDAR