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PRODID:Linklings LLC
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TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035348Z
LOCATION:Room 324\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221208T153000
DTEND;TZID=Asia/Seoul:20221208T170000
UID:siggraphasia_SIGGRAPH Asia 2022_sess166@linklings.com
SUMMARY:Image Editing and Manipulation
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\nNeRFFaceEd
 iting: Disentangled Face Editing in Neural Radiance Fields\n\nJiang, Chen,
  Liu, Fu, Gao\n\nRecent methods for synthesizing 3D-aware face images have
  achieved rapid development thanks to neural radiance fields, allowing for
  high quality and fast inference speed. However, existing solutions for ed
 iting facial geometry and appearance independently usually require retrain
 ing and are not optim...\n\n---------------------\nWater Simulation and Re
 ndering from a Still Photograph\n\nSugimoto, He, Liao, Sander\n\nWe propos
 e an approach to simulate and render realistic water animation from a sing
 le still input photograph. We first segment the water surface, estimate re
 ndering parameters, and compute water reflection textures with a combinati
 on of neural networks and traditional optimization techniques. Then w...\n
 \n---------------------\nVideoReTalking: Audio-based Lip Synchronization f
 or Talking Head Video Editing In the Wild\n\nCheng, Cun, Zhang, Xia, Yin..
 .\n\nWe present VideoReTalking, a new system to edit the faces of a real-w
 orld talking head video according to an input audio, producing a high-qual
 ity and lip-syncing output video even with a different emotion.Our system 
 disentangles this objective into three sequential tasks: (1) face video ge
 neration ...\n\n---------------------\nNeural Photo-Finishing\n\nTseng, Zh
 ang, Jebe, Zhang, Xia...\n\nImage processing pipelines are ubiquitous and 
 we rely on them either directly, by filtering or adjusting an image post-c
 apture, or indirectly, as image signal processing pipeline (ISP) on broadl
 y deployed camera systems. Used by artists, photographers, system engineer
 s, and for downstream vision tas...\n\n---------------------\nProduction-R
 eady Face Re-Aging for Visual Effects\n\nZoss, Chandran, Sifakis, Gross, G
 otardo...\n\nPhotorealistic digital re-aging of faces in video is becoming
  increasingly common in entertainment and advertising.  But the predominan
 t 2D painting workflow often requires frame-by-frame manual work that can 
 take days to accomplish, even by skilled artists.  Although research on fa
 cial image re-agi...\n\n---------------------\nStitch it in Time: GAN-Base
 d Facial Editing of Real Videos\n\nTzaban, Mokady, Gal, Bermano, Cohen-Or\
 n\nThe ability of Generative Adversarial Networks to encode rich semantics
  within their latent space has been widely adopted for facial image editin
 g. However, replicating their success with videos has proven challenging. 
 Applying StyleGAN editing over real videos introduces two main challenges:
  (i) St...\n\n---------------------\nTraining-Free Neural Matte Extraction
  for Visual Effects\n\nElcott, Lewis, Kanazawa, Bregler\n\nA deep neural n
 etwork-based alpha matte extraction approach that requires no training dat
 a, so is well-suited for VFX, where one-of-a-kind subjects appear so brief
 ly that gathering training data is pointless.\n\n\nRegistration Category: 
 FULL ACCESS, ON-DEMAND ACCESS\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON, O
 N-DEMAND
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