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BEGIN:VEVENT
DTSTAMP:20260114T163659Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T090000
DTEND;TZID=Australia/Melbourne:20231214T100500
UID:siggraphasia_SIGGRAPH Asia 2023_sess124@linklings.com
SUMMARY:Avatar Portrait
DESCRIPTION:NeRFFaceLighting: Implicit and Disentangled Face Lighting Repr
 esentation Leveraging Generative Prior in Neural Radiance Fields\n\n3D-awa
 re portrait lighting control is an emerging and promising domain, thanks t
 o the recent advance of generative adversarial networks and neural radianc
 e fields. Existing solutions typically try to decouple the lighting from t
 he geometry and appearance for disentangled control with an explicit lig..
 .\n\n\nKaiwen Jiang (Institute of Computing Technology, Chinese Academy of
  Sciences; Beijing Jiaotong University); Shu-Yu Chen (Institute of Computi
 ng Technology, Chinese Academy of Sciences); Hongbo Fu (School of Creative
  Media, City University of Hong Kong); and Lin Gao (Institute of Computing
  Technology, Chinese Academy of Sciences; University of Chinese Academy of
  Sciences)\n---------------------\nAvatarStudio: Text-driven Editing of 3D
  Dynamic Human Head Avatars\n\nCapturing and editing full head performance
 s enables the creation of virtual characters with various applications suc
 h as extended reality and media production. The past few years witnessed a
  steep rise in the photorealism of human head avatars. Such avatars can be
  controlled through different input...\n\n\nMohit Mendiratta, Xingang Pan,
  Mohamed Elgharib, Kartik Teotia, and Mallikarjun B R (Max Planck Institut
 e for Informatics); Ayush Tewari (MIT CSAIL); Vladislav Golyanik (Max Plan
 ck Institute for Informatics); Adam Kortylewski (Max Planck Institute for 
 Informatics, University of Freiburg); and Christian Theobalt (Max Planck I
 nstitute for Informatics)\n---------------------\nAniPortraitGAN: Animatab
 le 3D Portrait Generation from 2D Image Collections\n\nPrevious animatable
  3D-aware GANs for human generation have primarily focused on either the h
 uman head or full body. However, head-only videos are relatively uncommon 
 in real life, and full body generation typically does not deal with facial
  expression control and still has challenges in generating ...\n\n\nYue Wu
  (Hong Kong University of Science and Technology), Sicheng Xu (Microsoft R
 esearch Asia), Jianfeng Xiang (Tsinghua University), Fangyun Wei (Microsof
 t Research Asia), Qifeng Chen (Hong Kong University of Science and Technol
 ogy), and Jiaolong Yang and Xin Tong (Microsoft Research Asia)\n----------
 -----------\nBakedAvatar: Baking Neural Fields for Real-Time Head Avatar S
 ynthesis\n\nSynthesizing photorealistic 4D human head avatars from videos 
 is essential for VR/AR, telepresence, and video game applications. Althoug
 h existing Neural Radiance Fields (NeRF)-based methods achieve high-fideli
 ty results, the computational expense limits their use in real-time applic
 ations. To overc...\n\n\nHao-Bin Duan (Beihang University); Miao Wang (Bei
 hang University, Zhongguancun Laboratory); Jin-Chuan Shi and Xu-Chuan Chen
  (Beihang University); and Yan-Pei Cao (Tencent)\n---------------------\nN
 eural Point-based Volumetric Avatar: Surface-guided Neural Points for Effi
 cient and Photorealistic Volumetric Head Avatar\n\nRendering photo-realist
 ic and vividly moving human heads is very important for pleasant and immer
 sive experience in AR/VR and video conferencing. However, existing methods
  usually struggle to model challenging facial regions (e.g., mouth interio
 r, eyes, hair/beard), resulting in unrealistic and blur...\n\n\nCong Wang 
 (Tsinghua University); Di Kang, Yan-Pei Cao, Linchao Bao, and Ying Shan (T
 encent); and Song-Hai Zhang (Tsinghua University)\n\nRegistration Category
 : Full Access\n\nSession Chair: Lin Gao (University of Chinese Academy of 
 Sciences)
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