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TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
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DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035347Z
LOCATION:Room 324\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221207T140000
DTEND;TZID=Asia/Seoul:20221207T153000
UID:siggraphasia_SIGGRAPH Asia 2022_sess159@linklings.com
SUMMARY:Stylization and Colorization
DESCRIPTION:Technical Communications, Technical Papers\n\nThe presentation
 s will be followed by a 30-min Interactive Discussion Session at Room 325-
 CD.\n\nThe Technical Papers program is the premier international forum for
  disseminating new scholarly work in computer graphics and interactive tec
 hniques. Technical Papers are published as a special issue of ACM Transact
 ions on Graphics. In addition to papers selected by the SIGGRAPH Asia 2022
  Technical Papers Jury, the conference presents papers that have been publ
 ished in ACM Transactions on Graphics during the past year. Accepted paper
 s adhere to the highest scientific standards.\n\nThe Technical Communicati
 ons program is a premier forum for presenting the latest developments and 
 research still in progress. Leading international experts in academia and 
 industry present work that showcase actual implementations of research ide
 as, works at the crossroads of computer graphics with computer vision, mac
 hine learning, HCI, VR, CAD, visualization, and many others.\n\nReference 
 Based Sketch Extraction via Attention Mechanism\n\nAshtari, Seo, Kang, Cha
 , Noh\n\nWe propose a model that extracts a sketch from a colorized image 
 in such a way that the extracted sketch has a line style similar to a give
 n reference sketch while preserving the visual content identically to the 
 colorized image. Authentic sketches drawn by artists have various sketch s
 tyles to add ...\n\n---------------------\nVToonify: Controllable High-Res
 olution Portrait Video Style Transfer\n\nYang, Jiang, Liu, Loy\n\nGenerati
 ng high-quality artistic portrait videos is an important and desirable tas
 k in computer graphics and vision. Although a series of successful portrai
 t image toonification models built upon the powerful StyleGAN have been pr
 oposed, these image-oriented methods have obvious limitations when app...\
 n\n---------------------\nStyleBin: Stylizing Video by Example in Stereo\n
 \nKučera, Mould, Sýkora\n\nIn this paper we present StyleBin---an approach
  to example-based stylization of videos that can produce consistent binocu
 lar depiction of stylized content on stereoscopic displays. Given the targ
 et sequence and a set of stylized keyframes accompanied by information abo
 ut depth in the scene, we formu...\n\n---------------------\nWebtoonMe: A 
 Data-Centric Approach for Full-Body Portrait Stylization\n\nBack, Kim, Ahn
 \n\nA production-level full-body portrait stylization system.\nThe previou
 s method produces less plausible results and it cannot reflect diverse ski
 n tones.\nOur novel dataset preparation approach to alleviate aforemention
 ed issues effectively.\n\n---------------------\nDisentangled Image Colori
 zation via Global Anchors\n\nXia, Hu, Wong, Wang\n\nColorization is multim
 odal by nature and challenges existing frameworks to achieve colorful and 
 structurally consistent results. Even the sophisticated autoregressive mod
 el struggles to maintain long-distance color consistency due to the fragil
 ity of sequential dependence.\nTo overcome this challenge...\n\n----------
 -----------\nMyStyle: A Personalized Generative Prior\n\nNitzan, Aberman, 
 Liba, He, Yarom...\n\nWe introduce MyStyle, a personalized deep generative
  prior trained with a few shots of an individual. MyStyle allows to recons
 truct, enhance and edit images of a specific person, such that the output 
 is faithful to the person’s key facial characteristics. Given a small refe
 rence set of portrai...\n\n---------------------\nUniColor: A Unified Fram
 ework for Multi-Modal Colorization with Transformer\n\nHuang, Zhao, Liao\n
 \nWe propose the first unified framework UniColor to support colorization 
 in multiple modalities, including both unconditional and conditional ones,
  such as stroke, exemplar, text, and even a mix of them. Rather than learn
 ing a separate model for each type of condition, we introduce a two-stage 
 colori...\n\n\nRegistration Category: FULL ACCESS, ON-DEMAND ACCESS\n\nLan
 guage: ENGLISH\n\nFormat: IN-PERSON, ON-DEMAND
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