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DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (1)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241205T132300
DTEND;TZID=Asia/Tokyo:20241205T133400
UID:siggraphasia_SIGGRAPH Asia 2024_sess130_papers_509@linklings.com
SUMMARY:Controllable Shape Modeling with Neural Generalized Cylinder
DESCRIPTION:Technical Papers\n\nXiangyu Zhu (Chinese University of Hong Ko
 ng, Shenzhen); Zhiqin Chen (Adobe Research); Ruizhen Hu (Shenzhen Universi
 ty (SZU)); and Xiaoguang Han (Chinese University of Hong Kong, Shenzhen)\n
 \nNeural shape representation, such as neural signed distance field (NSDF)
 , becomes more and more popular in shape modeling as its ability to deal w
 ith complex topology and arbitrary resolution. Due to the implicit manner 
 to use features for shape representation, manipulating the shapes faces in
 herent challenge of inconvenience, since the feature cannot be intuitively
  edited. In this work, we propose neural generalized cylinder (NGC) for ex
 plicit manipulation of NSDF, which is an extension of traditional generali
 zed cylinder (GC). Specifically, we define a central curve first and assig
 n neural features along the curve to represent the profiles. Then NSDF is 
 defined on the relative coordinates of a specialized GC with oval-shaped p
 rofiles. By using the relative coordinates, NSDF can be explicitly control
 led via manipulation of the GC. To this end, we apply NGCto many non-rigid
  deformation tasks like complex curved deformation, local scaling and twis
 ting for shapes. The comparison on shape deformation with other methods pr
 oves the effectiveness and efficiency of NGC. Furthermore, NGC could utili
 ze the neural feature for shape blending by a simple neural feature interp
 olation.\n\nRegistration Category: Full Access, Full Access Supporter\n\nL
 anguage Format: English Language\n\nSession Chair: Noam Aigerman (Universi
 ty of Montreal)
URL:https://asia.siggraph.org/2024/program/?id=papers_509&sess=sess130
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