BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Asia/Tokyo
X-LIC-LOCATION:Asia/Tokyo
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:JST
DTSTART:18871231T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241204T114300
DTEND;TZID=Asia/Tokyo:20241204T115400
UID:siggraphasia_SIGGRAPH Asia 2024_sess113_tog_111@linklings.com
SUMMARY:ReN Human: Learning Relightable Neural Implicit Surfaces for Anima
 table Human Rendering
DESCRIPTION:Technical Papers\n\nRengan Xie (State Key Laboratory of CAD&CG
 , Zhejiang University); Kai Huang (Institute of Computing Technology, Chin
 ese Academy of Sciences; Zhejiang Lab); In-Young Cho (KRAFTON); Sen Yang (
 Zhejiang Lab); Wei Chen, Hujun Bao, and Wenting Zheng (State Key Laborator
 y of CAD&CG, Zhejiang University); Rong Li (Zhejiang University); and Yuch
 i Huo (State Key Laboratory of CAD&CG, Zhejiang University; Zhejiang Lab)\
 n\nThis work proposes ReN Human, a framework that utilizes sparse or even 
 monocular input videos to reconstruct a 3D human model represented as a de
 formable implicit neural surface. It decomposes geometry and material, res
 ulting in a relightable, animatable human model that can be rendered with 
 novel views, poses, and lighting.\n\nRegistration Category: Full Access, F
 ull Access Supporter\n\nLanguage Format: English Language\n\nSession Chair
 : Forrester Cole (Google)
URL:https://asia.siggraph.org/2024/program/?id=tog_111&sess=sess113
END:VEVENT
END:VCALENDAR