BEGIN:VCALENDAR VERSION:2.0 PRODID:Linklings LLC BEGIN:VTIMEZONE TZID:Asia/Tokyo X-LIC-LOCATION:Asia/Tokyo BEGIN:STANDARD TZOFFSETFROM:+0900 TZOFFSETTO:+0900 TZNAME:JST DTSTART:18871231T000000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250110T023312Z LOCATION:Hall B5 (2)\, B Block\, Level 5 DTSTART;TZID=Asia/Tokyo:20241204T135800 DTEND;TZID=Asia/Tokyo:20241204T140900 UID:siggraphasia_SIGGRAPH Asia 2024_sess116_papers_970@linklings.com SUMMARY:Content-aware Tile Generation using Exterior Boundary Inpainting DESCRIPTION:Technical Papers\n\nSam Sartor and Pieter Peers (College of Wi lliam & Mary)\n\nWe present a novel and flexible learning-based method for generating tileable image sets. Our method goes beyond simple self-tilin g, supporting sets of mutually tileable images that exhibit a high degree of diversity. To promote diversity we decouple structure from content by foregoing explicit copying of patches from an exemplar image. Instead we leverage the prior knowledge of natural images and textures embedded in la rge-scale pretrained diffusion models to guide tile generation constrained by exterior boundary conditions and a text prompt to specify the content. By carefully designing and selecting the exterior boundary conditions, we can reformulate the tile generation process as an inpainting problem, all owing us to directly employ existing diffusion-based inpainting models wit hout the need to retrain a model on a custom training set. We demonstrate the flexibility and efficacy of our content-aware tile generation method o n different tiling schemes, such as Wang tiles, from only a text prompt. Furthermore, we introduce a novel Dual Wang tiling scheme that provides gr eater texture continuity and diversity than existing Wang tile variants.\n \nRegistration Category: Full Access, Full Access Supporter\n\nLanguage Fo rmat: English Language\n\nSession Chair: Dani Lischinski (Hebrew Universit y of Jerusalem, Google) URL:https://asia.siggraph.org/2024/program/?id=papers_970&sess=sess116 END:VEVENT END:VCALENDAR