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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_157@linklings.com
SUMMARY:VToonify: Controllable High-Resolution Portrait Video Style Transf
 er
DESCRIPTION:Technical Papers\n\nVToonify: Controllable High-Resolution Por
 trait Video Style Transfer\n\nYang, Jiang, Liu, Loy\n\nGenerating high-qua
 lity artistic portrait videos is an important and desirable task in comput
 er graphics and vision. Although a series of successful portrait image too
 nification models built upon the powerful StyleGAN have been proposed, the
 se image-oriented methods have obvious limitations when applied to videos,
  such as the fixed frame size, the requirement of face alignment, missing 
 non-facial details and temporal inconsistency. In this work, we investigat
 e the challenging controllable high-resolution portrait video style transf
 er by introducing a novel VToonify framework. Specifically, VToonify lever
 ages the mid- and high-resolution layers of StyleGAN to render high-qualit
 y artistic portraits based on the multi-scale content features extracted b
 y an encoder to better preserve the frame details. The resulting fully con
 volutional architecture accepts non-aligned faces in videos of variable si
 ze as input, contributing to complete face regions with natural motions in
  the output. Our framework is compatible with existing StyleGAN-based imag
 e toonification models to extend them to video toonification, and inherits
  appealing features of these models for flexible style control on color an
 d intensity. This work presents two instantiations of VToonify built upon 
 Toonify and DualStyleGAN for collection-based and exemplar-based portrait 
 video style transfer, respectively. Extensive experimental results demonst
 rate the effectiveness of our proposed VToonify framework over existing me
 thods in generating high-quality and temporally-coherent artistic portrait
  videos with flexible style controls.\n\nRegistration Category: FULL ACCES
 S, EXPERIENCE PLUS ACCESS, EXPERIENCE ACCESS, TRADE EXHIBITOR\n\nLanguage:
  ENGLISH\n\nFormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_157&sess=sess15
 3
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