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DTSTAMP:20260114T163633Z
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_612@linklings.com
SUMMARY:Neural Point-based Volumetric Avatar: Surface-guided Neural Points
  for Efficient and Photorealistic Volumetric Head Avatar
DESCRIPTION:Cong Wang (Tsinghua University); Di Kang, Yan-Pei Cao, Linchao
  Bao, and Ying Shan (Tencent); and Song-Hai Zhang (Tsinghua University)\n\
 nRendering photo-realistic and vividly moving human heads is very importan
 t for pleasant and immersive experience in AR/VR and video conferencing. H
 owever, existing methods usually struggle to model challenging facial regi
 ons (e.g., mouth interior, eyes, hair/beard), resulting in unrealistic and
  blurry results. In this paper, we propose Neural Point-based Volumetric A
 vatar (NPVA), which discards predefined connectivity and hard corresponden
 ce imposed by mesh-based methods (i.e. neural points) and adopts neural vo
 lume rendering. Specifically, the neural points are constrained around the
  surface of the target expression via a high-resolution UV displacement ma
 p, achieving increased modeling capacity and more accurate control. We pro
 pose three technical innovations to improve the rendering and training eff
 iciency, including a patch-wise depth-guided (shading point) sampling stra
 tegy, a lightweight radiance decoding process, and a Grid-Error-Patch (GEP
 ) ray sampling strategy during training. By design, our NPVA can better ha
 ndle topologically changing regions and thin structures, and can be animat
 ed with accurate expression control.\n\nRegistration Category: Full Access
 , Enhanced Access, Trade Exhibitor, Experience Hall Exhibitor\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_612&sess=sess209
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