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BEGIN:VEVENT
DTSTAMP:20260114T163631Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_810@linklings.com
SUMMARY:Text-Guided Vector Graphics Customization
DESCRIPTION:Peiying Zhang (City University of Hong Kong), Nanxuan Zhao (Ad
 obe Research), and Jing Liao (City University of Hong Kong)\n\nVector grap
 hics are widely used in digital art and valued by designers for their scal
 ability and layer-wise topological properties. However, the creation and e
 diting of vector graphics necessitate creativity and design expertise, lea
 ding to a time-consuming process. In this paper, we propose a novel pipeli
 ne that generates high-quality customized vector graphics based on\ntextua
 l prompts while preserving the properties and layer-wise information of a 
 given exemplar SVG. Our method harnesses the capabilities of large pre-tra
 ined text-to-image models. By fine-tuning the cross-attention layers of th
 e model, we generate customized raster images guided by textual prompts. T
 o initialize the SVG, we introduce a semantic-based path alignment method 
 that preserves and transforms crucial paths from the exemplar SVG. Additio
 nally, we optimize path parameters using both image-level and vector-level
  losses, ensuring smooth shape deformation while aligning with the customi
 zed raster image. We extensively evaluate our method using multiple metric
 s from vector-level, image-level, and text-level perspectives. The evaluat
 ion results demonstrate the effectiveness of our pipeline in generating di
 verse customizations of vector graphics with exceptional quality. The proj
 ect page is https://intchous.github.io/SVGCustomization.\n\nRegistration C
 ategory: Full Access, Enhanced Access, Trade Exhibitor, Experience Hall Ex
 hibitor\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_810&sess=sess209
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