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BEGIN:VEVENT
DTSTAMP:20240214T070311Z
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:Technical Papers, TOG\n\nDrivable Avatar Clothing: Faithful Fu
 ll-Body Telepresence with Dynamic Clothing Driven by Sparse RGB-D Input\n\
 nClothing is an important part of human appearance but challenging to mode
 l in photorealistic avatars. In this work we present avatars with dynamica
 lly moving loose clothing that can be faithfully driven by sparse RGB-D in
 puts as well as body and face motion. We propose a Neural Iterative Closes
 t Poi...\n\n\nDonglai Xiang (Carnegie Mellon University/Robotics Institute
 , Meta Reality Labs Research); Fabian Prada, Zhe Cao, Kaiwen Guo, and Chen
 glei Wu (Meta Reality Labs Research); Jessica Hodgins (Carnegie Mellon Uni
 versity); and Timur Bagautdinov (Meta Reality Labs Research)\n------------
 ---------\nSAILOR: Synergizing Radiance and Occupancy Fields for Live Huma
 n Performance Capture\n\nImmersive user experiences in live VR/AR performa
 nces require a fast and accurate free-view rendering of the performers. Ex
 isting methods are mainly based on Pixel-aligned Implicit Functions (PIFu)
  or Neural Radiance Fields (NeRF). However, while PIFu-based methods usual
 ly fail to produce photoreali...\n\n\nZheng Dong (State Key Laboratory of 
 CAD & CG, Zhejiang University); Ke Xu (City University of Hong Kong); Yaoa
 n Gao (State Key Laboratory of CAD & CG, Zhejiang University); Qilin Sun (
 The Chinese University of Hong Kong, Shenzhen); Hujun Bao and Weiwei Xu (S
 tate Key Laboratory of CAD & CG, Zhejiang University); and Rynson W.H. Lau
  (City University of Hong Kong)\n---------------------\nHigh-Resolution Vo
 lumetric Reconstruction for Clothed Humans\n\nWe present a novel method fo
 r reconstructing clothed humans from a sparse set of, e.g., 1-6 RGB images
 . We revisit the volumetric approach and demonstrate that better performan
 ce can be achieved with proper system design. The volumetric representatio
 n offers significant advantages in leveraging 3D s...\n\n\nSicong Tang (Si
 mon Fraser University); Guangyuan Wang, Qing Ran, Lingzhi Li, and Li Shen 
 (Alibaba); and Ping Tan (Simon Fraser University)\n---------------------\n
 FLARE: Fast Learning of Animatable and Relightable Mesh Avatars\n\nOur goa
 l is to efficiently learn personalized animatable 3D head avatars from vid
 eos that are geometrically accurate, realistic, relightable, and compatibl
 e with current rendering systems. While 3D meshes enable efficient process
 ing and are highly portable, they lack realism in terms of shape and ap...
 \n\n\nShrisha Bharadwaj (Max Planck Institute for Intelligent Systems); Yu
 feng Zheng (ETH Zürich, Max Planck Institute for Intelligent Systems); Otm
 ar Hilliges (ETH Zürich); and Michael Black and Victoria Fernandez Abrevay
 a (Max Planck Institute for Intelligent Systems)\n---------------------\nT
 owards Practical Capture of High-Fidelity Relightable Avatars\n\nIn this p
 aper, we propose a novel framework, Tracking-free Relightable Avatar (TRAv
 atar), for capturing and reconstructing high-fidelity 3D avatars. Compared
  to previous methods, TRAvatar works in a more practical and efficient set
 ting. Specifically, TRAvatar is trained with dynamic image sequences ...\n
 \n\nHaotian Yang, Mingwu Zheng, Wanquan Feng, and Haibin Huang (Kuaishou T
 echnology); Yu-Kun Lai (Cardiff University); and Pengfei Wan, Zhongyuan Wa
 ng, and Chongyang Ma (Kuaishou Technology)\n\nRegistration Category: Full 
 Access\n\nSession Chair: Parag Chaudhuri (Indian Institute of Technology B
 ombay)
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