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BEGIN:VEVENT
DTSTAMP:20260114T163713Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T090000
DTEND;TZID=Australia/Melbourne:20231214T100500
UID:siggraphasia_SIGGRAPH Asia 2023_sess148@linklings.com
SUMMARY:Texture Magic
DESCRIPTION:Joint UV Optimization and Texture Baking\n\nLevel of detail (L
 OD) has been widely used in interactive computer graphics. In current indu
 strial 3D modeling pipelines, artists rely on commercial software to gener
 ate highly detailed models with UV maps, and then bake textures for low-po
 ly counterparts. In these pipelines, each step is performed ...\n\n\nJulia
 n Knodt, Zherong Pan, Kui Wu, and Xifeng Gao (LightSpeed Studios)\n-------
 --------------\nEXIM: A Hybrid Explicit-Implicit Representation for Text-G
 uided 3D Shape Generation\n\nThis paper presents a new text-guided techniq
 ue for generating 3D shapes. The technique leverages a hybrid 3D shape rep
 resentation, combining the strengths of explicit and implicit representati
 ons. Specifically, the explicit stage controls the generated topology of t
 he 3D shape and allows local mani...\n\n\nZhengzhe Liu, Jingyu Hu, and Ka-
 Hei Hui (The Chinese University of Hong Kong); Xiaojuan Qi (The University
  of Hong Kong); Daniel Cohen-Or (Tel-Aviv University); and Chi-Wing Fu (Th
 e Chinese University of Hong Kong)\n---------------------\nTwinTex: Geomet
 ry-aware Texture Generation for Abstracted 3D Architectural Models\n\nCoar
 se architectural models are often generated at scales ranging from individ
 ual buildings to scenes for downstream applications such as Digital Twin C
 ity, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted
  as twins from 3D dense reconstructions. However, these models typically l
 ...\n\n\nWeidan Xiong, Hongqian Zhang, Botao Peng, Ziyu Hu, Yongli Wu, Jia
 nwei Guo, and Hui Huang (Shenzhen University)\n---------------------\nHype
 rDreamer: Hyper-Realistic 3D Content Generation and Editing from a Single 
 Image\n\n3D content creation from a single image is a long-standing yet hi
 ghly desirable task. Recent advances introduce 2D diffusion priors, yieldi
 ng reasonable results. However, existing methods are not hyper-realistic e
 nough for post-generation usage, as users cannot view, render and edit the
  resulting 3D...\n\n\nTong Wu and Zhibing Li (The Chinese University of Ho
 ng Kong, Shanghai AI Laboratory); Shuai Yang (Shanghai Jiao Tong Universit
 y, Shanghai AI Laboratory); Pan Zhang (Shanghai AI Laboratory); Xingang Pa
 n (Max Planck Institute for Informatics); Jiaqi Wang (Shanghai AI Laborato
 ry); Dahua Lin (The Chinese University of Hong Kong, Shanghai AI Laborator
 y); and Ziwei Liu (Nanyang Technological University)\n--------------------
 -\nTexture Atlas Compression Based on Repeated Content Removal\n\nOptimizi
 ng the memory footprint of 3D models can have a major impact on the user e
 xperiences during real-time rendering and streaming visualization, where t
 he major memory overhead lies in the high-resolution texture data. In this
  work, we propose a robust and automatic pipeline to content-aware, lo...\
 n\n\nYuzhe Luo (State Key Laboratory of CAD & CG, Zhejiang University; LIG
 HTSPEED STUDIOS); Xiaogang Jin (State Key Laboratory of CAD & CG, Zhejiang
  University); Zherong Pan and Kui Wu (LIGHTSPEED STUDIOS); Qilong Kou and 
 Xiajun Yang (Tencent Technology (Shenzhen) Co., LTD); and Xifeng Gao (LIGH
 TSPEED STUDIOS)\n\nRegistration Category: Full Access\n\nSession Chair: Li
 n Lu (Shandong University)
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