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DTSTAMP:20260817T171530Z
LOCATION:Hall B5 (1)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241205T130000
DTEND;TZID=Asia/Tokyo:20241205T141000
UID:siggraphasia_SIGGRAPH Asia 2024_sess130@linklings.com
SUMMARY:Neural Shapes
DESCRIPTION:Each Paper gives a 10 minute presentation.\n\nSpaceMesh: A Con
 tinuous Representation for Learning Manifold Surface Meshes\n\nMeshes are 
 ubiquitous in visual computing and simulation, yet most existing machine l
 earning techniques represent meshes only indirectly, e.g. as the level set
  of a scalar field, or deformation of a template, or as a disordered trian
 gle soup lacking local structure. This work presents a scheme to di...\n\n
 \nTianchang Shen (University of Toronto, NVIDIA Research) and Zhaoshuo Li,
  Marc Law, Matan Atzmon, Sanja Fidler, James Lucas, Jun Gao, and Nicholas 
 Sharp (NVIDIA Research)\n---------------------\nNASM: Neural Anisotropic S
 urface Meshing\n\nThis paper introduces a new learning-based method, NASM,
  for anisotropic surface meshing. Our key idea is to propose a graph neura
 l network to embed an input mesh into a high-dimensional (high-d) Euclidea
 n embedding space to preserve curvature-based anisotropic metric by using 
 a dot product loss bet...\n\n\nHongbo Li, Haikuan Zhu, and Sikai Zhong (Wa
 yne State University); Ningna Wang (University of Texas at Dallas); Cheng 
 Lin (University of Hong Kong); Xiaohu Guo (University of Texas at Dallas);
  Shiqing Xin (Shandong University); Wenping Wang (Texas A&M University); a
 nd Jing Hua and Zichun Zhong (Wayne State University)\n-------------------
 --\nControllable Shape Modeling with Neural Generalized Cylinder\n\nNeural
  shape representation, such as neural signed distance field (NSDF), become
 s more and more popular in shape modeling as its ability to deal with comp
 lex topology and arbitrary resolution. Due to the implicit manner to use f
 eatures for shape representation, manipulating the shapes faces inherent..
 .\n\n\nXiangyu Zhu (Chinese University of Hong Kong, Shenzhen); Zhiqin Che
 n (Adobe Research); Ruizhen Hu (Shenzhen University (SZU)); and Xiaoguang 
 Han (Chinese University of Hong Kong, Shenzhen)\n---------------------\nDi
 rect Manipulation of Procedural Implicit Surfaces\n\nProcedural implicit s
 urfaces are a popular representation for shape modeling. They provide a si
 mple framework for complex geometric operations such as Booleans, blending
  and deformations. However, their editability remains a challenging task: 
 as the definition of the shape is purely implicit, direct...\n\n\nMarzia R
 iso (Sapienza University of Rome, Adobe); Élie Michel, Axel Paris, Valenti
 n Deschaintre, and Mathieu Gaillard (Adobe); and Fabio Pellacini (Universi
 ty of Modena and Reggio Emilia)\n---------------------\nNeural Laplacian O
 perator for 3D Point Clouds\n\nThe Laplacian operator holds a crucial role
  in 3D geometry processing, yet it is still challenging to define it on po
 int clouds.\nPrevious works mainly focused on constructing a local triangu
 lation around each point to approximate the underlying manifold for defini
 ng the Laplacian operator, which may...\n\n\nBo Pang, Zhongtian Zheng, Yil
 ong Li, Guoping Wang, and Peng-Shuai Wang (Peking University)\n-----------
 ----------\nSRIF: Semantic Shape Registration Empowered by Diffusion-based
  Image Morphing and Flow Estimation\n\nIn this paper, we propose \textbf{S
 RIF}, a novel \textbf{S}emantic shape \textbf{R}egistration framework base
 d on diffusion-based \textbf{I}mage morphing and \textbf{F}low Estimation.
  \nMore concretely, given a pair of extrinsically aligned shapes, we first
  render them from multi-views, and then we u...\n\n\nMingze Sun (Tsinghua 
 shenzhen international graduate school); Chen Guo and Puhua Jiang (Tsinghu
 a shenzhen international graduate school, Pengcheng Lab); and Shiwei Mao, 
 Yurun Chen, and Ruqi Huang (Tsinghua shenzhen international graduate schoo
 l)\n\nRegistration Category: Full Access, Full Access Supporter\n\nLanguag
 e Format: English Language\n\nSession Chair: Noam Aigerman (University of 
 Montreal, Mila)
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