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DTSTAMP:20260114T163643Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T140000
DTEND;TZID=Australia/Melbourne:20231213T150500
UID:siggraphasia_SIGGRAPH Asia 2023_sess164@linklings.com
SUMMARY:Full-Body Avatar
DESCRIPTION:Drivable Avatar Clothing: Faithful Full-Body Telepresence with
  Dynamic Clothing Driven by Sparse RGB-D Input\n\nClothing is an important
  part of human appearance but challenging to model in photorealistic avata
 rs. In this work we present avatars with dynamically moving loose clothing
  that can be faithfully driven by sparse RGB-D inputs as well as body and 
 face motion. We propose a Neural Iterative Closest Poi...\n\n\nDonglai Xia
 ng (Carnegie Mellon University/Robotics Institute, Meta Reality Labs Resea
 rch); Fabian Prada, Zhe Cao, Kaiwen Guo, and Chenglei Wu (Meta Reality Lab
 s Research); Jessica Hodgins (Carnegie Mellon University); and Timur Bagau
 tdinov (Meta Reality Labs Research)\n---------------------\nTowards Practi
 cal Capture of High-Fidelity Relightable Avatars\n\nIn this paper, we prop
 ose a novel framework, Tracking-free Relightable Avatar (TRAvatar), for ca
 pturing and reconstructing high-fidelity 3D avatars. Compared to previous 
 methods, TRAvatar works in a more practical and efficient setting. Specifi
 cally, TRAvatar is trained with dynamic image sequences ...\n\n\nHaotian Y
 ang, Mingwu Zheng, Wanquan Feng, and Haibin Huang (Kuaishou Technology); Y
 u-Kun Lai (Cardiff University); and Pengfei Wan, Zhongyuan Wang, and Chong
 yang Ma (Kuaishou Technology)\n---------------------\nHigh-Resolution Volu
 metric Reconstruction for Clothed Humans\n\nWe present a novel method for 
 reconstructing clothed humans from a sparse set of, e.g., 1-6 RGB images. 
 We revisit the volumetric approach and demonstrate that better performance
  can be achieved with proper system design. The volumetric representation 
 offers significant advantages in leveraging 3D s...\n\n\nSicong Tang (Simo
 n Fraser University); Guangyuan Wang, Qing Ran, Lingzhi Li, and Li Shen (A
 libaba); and Ping Tan (Simon Fraser University)\n---------------------\nFL
 ARE: Fast Learning of Animatable and Relightable Mesh Avatars\n\nOur goal 
 is to efficiently learn personalized animatable 3D head avatars from video
 s that are geometrically accurate, realistic, relightable, and compatible 
 with current rendering systems. While 3D meshes enable efficient processin
 g and are highly portable, they lack realism in terms of shape and ap...\n
 \n\nShrisha Bharadwaj (Max Planck Institute for Intelligent Systems); Yufe
 ng Zheng (ETH Zürich, Max Planck Institute for Intelligent Systems); Otmar
  Hilliges (ETH Zürich); and Michael Black and Victoria Fernandez Abrevaya 
 (Max Planck Institute for Intelligent Systems)\n---------------------\nSAI
 LOR: Synergizing Radiance and Occupancy Fields for Live Human Performance 
 Capture\n\nImmersive user experiences in live VR/AR performances require a
  fast and accurate free-view rendering of the performers. Existing methods
  are mainly based on Pixel-aligned Implicit Functions (PIFu) or Neural Rad
 iance Fields (NeRF). However, while PIFu-based methods usually fail to pro
 duce photoreali...\n\n\nZheng Dong (State Key Laboratory of CAD & CG, Zhej
 iang University); Ke Xu (City University of Hong Kong); Yaoan Gao (State K
 ey Laboratory of CAD & CG, Zhejiang University); Qilin Sun (The Chinese Un
 iversity of Hong Kong, Shenzhen); Hujun Bao and Weiwei Xu (State Key Labor
 atory of CAD & CG, Zhejiang University); and Rynson W.H. Lau (City Univers
 ity of Hong Kong)\n\nRegistration Category: Full Access\n\nSession Chair: 
 Parag Chaudhuri (Indian Institute of Technology Bombay)
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