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DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (1)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241204T144500
DTEND;TZID=Asia/Tokyo:20241204T155500
UID:siggraphasia_SIGGRAPH Asia 2024_sess118@linklings.com
SUMMARY:Threads of Reality: Garments & Knitting
DESCRIPTION:Technical Papers\n\nEach Paper gives a 10 minute presentation.
 \n\nUFO Instruction Graphs Are Machine Knittable\n\nProgramming low-level 
 controls for knitting machines is a meticulous, time-consuming task that d
 emands specialized expertise. Recently, there has been a shift towards aut
 omatically generating low-level knitting machine programs from high-level 
 knit representations that describe knit objects in a mor...\n\n\nJenny Lin
  (Carnegie Mellon University), Yuka Ikarashi (Massachusetts Institute of T
 echnology), Gilbert Bernstein (University of Washington), and James McCann
  (Carnegie Mellon University)\n---------------------\nChebyshev Parameteri
 zation for Woven Fabric Modeling\n\nDistortion-minimizing surface paramete
 rization is an essential step for computing 2D pieces necessary to fabrica
 te a target 3D shape from flat material. Garment design and textile fabric
 ation are a prominent application example. Common distortion measures quan
 tify length, angle or area preservation ...\n\n\nAnnika Oehri (ETH Zürich)
  and Aviv Segall, Jing Ren, and Olga Sorkine-Hornung (ETH Zurich)\n-------
 --------------\nFabricDiffusion: High-Fidelity Texture Transfer for 3D Gar
 ments Generation from In-The-Wild Images\n\nWe introduce FabricDiffusion, 
 a method for transferring fabric textures from a single clothing image to 
 3D garments of arbitrary shapes. Existing approaches typically synthesize 
 textures on the garment surface through 2D-to-3D texture mapping or depth-
 aware inpainting via generative models. Unfortun...\n\n\nCheng Zhang (Carn
 egie Mellon University, Texas A&M University); Yuanhao Wang and Francisco 
 Vicente (Carnegie Mellon University); Chenglei Wu, Jinlong Yang, and Thabo
  Beeler (Google Inc.); and Fernando De la Torre (Carnegie Mellon Universit
 y)\n---------------------\nGarVerseLOD: High-Fidelity 3D Garment Reconstru
 ction from a Single In-the-Wild Image using a Dataset with Levels of Detai
 ls\n\nNeural implicit functions have brought impressive advances to the st
 ate-of-the-art of clothed human digitization from multiple or even single 
 images. However, despite the progress, current arts still have difficulty 
 generalizing to unseen images with complex cloth deformation and body pose
 s. In this...\n\n\nZhongjin Luo, Haolin Liu, Chenghong Li, Wanghao Du, Zir
 ong Jin, and Wanhu Sun (Chinese University of Hong Kong, Shenzhen); Yinyu 
 Nie (Huawei Technologies Ltd.); Weikai Chen (Tencent America); and Xiaogua
 ng Han (Chinese University of Hong Kong, Shenzhen)\n---------------------\
 nVolumetric Homogenization for Knitwear Simulation\n\nWe present volumetri
 c homogenization,  a spatially varying homogenization scheme for knitwear 
 simulation. We are motivated by the observation that macro-scale fabric dy
 namics is strongly correlated with its underlying knitting patterns. There
 fore, homogenization towards a single\nmaterial is less eff...\n\n\nChun Y
 uan, Haoyang Shi, and Lei Lan (University of Utah); Yuxing Qiu (LightSpeed
  Studios); Cem Yuksel (University of Utah); Huamin Wang (Style3D Research)
 ; Chenfanfu Jiang (University of California Los Angeles); Kui Wu (LightSpe
 ed Studios); and Yin Yang (University of Utah)\n\nRegistration Category: F
 ull Access, Full Access Supporter\n\nLanguage Format: English Language\n\n
 Session Chair: Meng Zhang (Nanjing University of Science and Technology)
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