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DTSTAMP:20260805T205503Z
LOCATION:Room 403  B
DTSTART;TZID=America/Los_Angeles:20260722T090000
DTEND;TZID=America/Los_Angeles:20260722T103000
UID:siggraph_SIGGRAPH 2026_sess119@linklings.com
SUMMARY:Hair, Fur & Feathers
DESCRIPTION:HairGPT: A Unified Autoregressive Framework for 3D Realistic H
 airstyle Synthesis\n\nHairGPT introduces a paradigm shift toward strand-as
 -language modeling, reformulating 3D realistic hair generation as a dual-d
 ecoupled autoregressive process where strands are treated as the fundament
 al generative units.\n\n\nHaimin Luo (ShanghaiTech University); min Ouyang
  (ShanghaiTech University; Deemos Technology Co., Ltd.); and Lan Xu and Ji
 ngyi Yu (ShanghaiTech University)\n---------------------\nHairLRM: Strand-
 based Hair Modeling via Large Reconstruction Models\n\nHairLRM integrates 
 Large Reconstruction Model (LRM) geometric priors into strand generation. 
 Leveraging an LRM mesh anchor and a Dual Orientation AutoEncoder, it trans
 forms coarse geometry into high-fidelity strands, disentangling complex to
 pologies to set a new benchmark for robust, accurate hair r...\n\n\nYuefan
  Shen (LIGHTSPEED); Yican Dong (State Key Laboratory of CAD & CG, Zhejiang
  University); Xiufeng Huang (Hong Kong Baptist University); Zhongtian Zhen
 g (LIGHTSPEED); Youyi Zheng (State Key Laboratory of CAD & CG, Zhejiang Un
 iversity); and Kui Wu (LIGHTSPEED)\n---------------------\nHairPort: In-co
 ntext 3D-aware Hair Import and Transfer for Images\n\nHairPort is a 3D-awa
 re hairstyle transfer method that faithfully moves a reference hairstyle o
 nto a source face image, even under large pose and scale differences. By s
 eparating hair removal from synthesis and enforcing geometric consistency 
 through 3D reconstruction, HairPort achieves accurate, ide...\n\n\nAlireza
  Heidari and Amirhossein Alimohammadi (Simon Fraser University), Wallace M
 ichel Pinto Lira and Adi Bar-Lev (Huawei Canada), and Ali Mahdavi-Amiri (S
 imon Fraser University)\n---------------------\nCurvature Space Editing of
  Highly-Coiled Hair\n\nWe use the material curvatures of the super-helix m
 odel to better analyze and edit tightly coiled hair. Within this curvature
  space, we propose novel operations for hair ruffling, heuristics for trac
 king flips in handedness, and control armatures that enable intuitive ense
 mble strand editing.\n\n\nAlvin Shi (Yale University); Florence Bertails-D
 escoubes (INRIA, France); A.M. Darke (University of California Santa Cruz)
 ; and Theodore Kim (Yale University)\n---------------------\nEfficient Fur
  and Hair Multiple Scattering Using Volumetric Approximation\n\nWe introdu
 ce a hybrid framework for rendering dense hair and fur. By preserving fine
  geometric details for direct illumination while approximating complex mul
 tiple scattering within an equivalent anisotropic volume, our method achie
 ves path-traced visual fidelity and soft glow of multiple scattering...\n\
 n\nRuike Hu (Nanjing University), Junqiu Zhu (Shandong University), Mingha
 o Lin (Shandong Universityong), Ruian Zhang (Nanjing University), Lu Wang 
 (Shandong University), Jie Guo and Yanwen Guo (Nanjing University), and Li
 ngqi Yan (MBZUAI)\n---------------------\nHair, Fur & Feathers - Interacti
 ve Discussion\n\nAfter the summary presentations, attendees will participa
 te in an interactive discussion. Outside the room will be a series of post
 er boards for authors to gather around with the audience. Authors are invi
 ted to bring any material related to their paper that could instigate furt
 her conversation such...\n\n\nInterest Area: Research & Education\n\nKeywo
 rd: Artificial Intelligence/Machine Learning, Geometry, Modeling, Renderin
 g\n\nRegistration Category: Full Conference Supporter, Full Conference\n\n
 Session Chair: Etienne Vouga (University of Texas Austin)
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