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DTSTAMP:20260805T205507Z
LOCATION:Room 403  B
DTSTART;TZID=America/Los_Angeles:20260723T140000
DTEND;TZID=America/Los_Angeles:20260723T153000
UID:siggraph_SIGGRAPH 2026_sess117@linklings.com
SUMMARY:Garments
DESCRIPTION:LoBoFit: Flexible Garment Refitting via Local Bone Mapping Ble
 nding\n\nLoBoFit brings robust, high-fidelity garment refitting to challen
 ging avatar changes. It preserves original design features, fine wrinkles,
  and fit style by optimizing garments in blended local bone coordinates, e
 nabling stable convergence, efficient refinement, and reliable results acr
 oss large bod...\n\n\nMeng Zhang (Nanjing University of Science and Techno
 logy), Yu Xin (University of Science and Technology of China), Feiya Guo (
 Nanjing University of Science and Technology), Kaizhang Kang (King Abdulla
 h University of Science and Technology (KAUST)), Mengyu Chu (Peking Univer
 sity), and Ruizhen Hu (Shenzhen University)\n---------------------\nPatter
 nGSL: A Structured Specification Language for Template-Free and Simulation
 -Ready 3D Garments\n\nWe present PatternGSL, a structured and template-fre
 e representation for simulation-ready 3D garments. Given a single garment 
 image, our VLM-based method predicts editable sewing patterns with explici
 t panel, edge, and stitch topology. Trained on the large-scale PatternGSLD
 ata dataset, it enables ac...\n\n\nZhenyang Li (The University of Hong Kon
 g (HKU); Tsinghua University, China); Lutao Jiang (The Hong Kong Universit
 y of Science and Technology (Guangzhou)); Yizhou Zhao (Carnegie Mellon Uni
 versity); Weikai Chen (LIGHTSPEED); Ying-Cong Chen (The Hong Kong Universi
 ty of Science and Technology (Guangzhou), The Hong Kong University of Scie
 nce and Technology); Xin Wang (LIGHTSPEED); and Yifan Peng (The University
  of Hong Kong (HKU))\n---------------------\nGarment Particles: A 2D–3D Sy
 mmetric Garment Representation for Generation and Editing\n\nGarment Parti
 cles is a 5D point-cloud representation that jointly encodes 2D sewing pat
 terns and 3D geometry. This representation enables Garment Particles Flow 
 (GPF) and Particles-to-Pattern Flow (PPF), a rectified flow framework for 
 both simulation-ready sewing pattern generation from text and ima...\n\n\n
 Kiyohiro Nakayama (Stanford University); I-Chao Shen (The University of To
 kyo); Ruofan Liu (Institute of Science Tokyo, Stanford University); Yiming
  Wang (ETH Zürich, Stanford University); Gordon Wetzstein (Stanford Univer
 sity); and Takeo Igarashi (The University of Tokyo)\n---------------------
 \nLearning Sewing Patterns via Latent Flow Matching of Implicit Fields\n\n
 We introduce an implicit representation for sewing patterns using distance
  fields. Combined with latent flow matching, the model learns distribution
 s over panel geometry and stitching, enabling generation, image-based esti
 mation, and optimization. The framework supports flexible topology and imp
 rove...\n\n\nCong Cao (MBZUAI); Ren Li (MBZUAI, SUSTech); Corentin Dumery 
 (EPFL); and Hao Li (Mohamed Bin Zayed University of Artificial Intelligenc
 e)\n---------------------\nLearned Universal Interoperable Virtual Try-ON\
 n\nWe introduce a fully automated 3D virtual try-on system that fits diver
 se multilayer garments onto arbitrary humanoid characters. Using SMPL as a
 n intermediate proxy, it handles unrigged, non-manifold assets, supports g
 arment resizing, and produces high-quality draping without manual correspo
 ndence,...\n\n\nCong Cao and Xianhang Cheng (Mohamed Bin Zayed University 
 of Artificial Intelligence); Jingyuan Liu (The University of Tokyo, Mohame
 d Bin Zayed University of Artificial Intelligence); Yujian Zheng and Zhenh
 ui Lin (Mohamed Bin Zayed University of Artificial Intelligence); Ren Li (
 Mohamed Bin Zayed University of Artificial Intelligence, SUSTech); Meriem 
 Chkir (Mohamed Bin Zayed University of Artificial Intelligence); and Hao L
 i (Mohamed Bin Zayed University of Artificial Intelligence, Pinscreen)\n--
 -------------------\nFIT: A Large-Scale Dataset for Fit-Aware Virtual Try-
 On\n\nWe introduce FIT (Fit-Inclusive Try-on), a virtual try-on dataset of
  over 1M samples covering diverse garment fits, each annotated with precis
 e body/garment measurements. Our novel data generation pipeline leverages 
 synthetic garment simulation and photorealistic retexturing. Then, we leve
 rage FIT t...\n\n\nYuanhao Wang and Johanna Karras (University of Washingt
 on, Google Research); Yingwei Li (Google Research); and Ira Kemelmacher-Sh
 lizerman (University of Washington, Google Research)\n--------------------
 -\nGarments - Interactive Discussion\n\nAfter the summary presentations, a
 ttendees will participate in an interactive discussion. Outside the room w
 ill be a series of poster boards for authors to gather around with the aud
 ience. Authors are invited to bring any material related to their paper th
 at could instigate further conversation such...\n\n\nInterest Area: Resear
 ch & Education\n\nKeyword: Animation, Artificial Intelligence/Machine Lear
 ning, Geometry, Modeling, Simulation\n\nRegistration Category: Full Confer
 ence Supporter, Full Conference\n\nSession Chair: Biao Zhang (Xi'an Jiaoto
 ng University)
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