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DTSTART:19721003T020000
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DTSTAMP:20260114T163653Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T140000
DTEND;TZID=Australia/Melbourne:20231214T150000
UID:siggraphasia_SIGGRAPH Asia 2023_sess151@linklings.com
SUMMARY:Neural Physics
DESCRIPTION:ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi
 -Layered Clothes\n\nWe present ClothCombo, a pipeline to drape arbitrary c
 ombinations of clothes on 3D human models with varying body shapes and pos
 es. While existing learning-based approaches for draping clothes have show
 n promising results, multi-layered clothing remains challenging as it is n
 on-trivial to model inte...\n\n\nDohae Lee, Hyun Kang, and In-Kwon Lee (Yo
 nsei University)\n---------------------\nNeural Collision Fields for Trian
 gle Primitives\n\nWe present neural collision fields as an alternative to 
 contact point sampling in physics simulations.\nOur approach is built on t
 op of a novel smoothed integral formulation for the contact surface patche
 s between two triangle meshes. By reformulating collisions as an integral,
  we avoid issues of sam...\n\n\nRyan Zesch (Texas A&M University), Vismay 
 Modi (University of Toronto), Shinjiro Sueda (Texas A&M University), and D
 avid Levin (University of Toronto)\n---------------------\nLearning Contac
 t Deformations with General Collider Descriptors\n\nThis paper presents a 
 learning-based method for the simulation of rich contact deformations on r
 educed deformation models. Previous works learn deformation models for spe
 cific pairs of objects, and we lift this limitation by designing a neural 
 model that supports general rigid collider shapes. We do...\n\n\nCristian 
 Romero and Dan Casas (Universidad Rey Juan Carlos) and Maurizio Chiaramont
 e and Miguel A. Otaduy (Meta Reality Labs Research)\n---------------------
 \nFluid Simulation on Neural Flow Maps\n\nWe introduce Neural Flow Maps, a
  novel simulation method bridging the emerging paradigm of implicit neural
  representations with fluid simulation based on the theory of flow maps, t
 o achieve state-of-the-art simulation of inviscid fluid phenomena. We devi
 se a novel hybrid neural field representation,...\n\n\nYitong Deng (Dartmo
 uth College), Hong-Xing Yu (Stanford University), Diyang Zhang (Dartmouth 
 College), Jiajun Wu (Stanford University), and Bo Zhu (Dartmouth College)\
 n---------------------\nNeural Stress Fields for Reduced-order Elastoplast
 icity and Fracture\n\nThe material point method (MPM) is a versatile simul
 ation framework for large-deformation elastoplasticity and fracture. Howev
 er, MPM's long runtime and large memory consumptions render it unsuitable 
 for applications constrained by computation time and memory usage, e.g., v
 irtual reality. To overcom...\n\n\nZeshun Zong and Xuan Li (University of 
 California Los Angeles); Minchen Li (University of California Los Angeles,
  Carnegie Mellon University); Maurizio M. Chiaramonte (Meta Reality Labs R
 esearch); Wojciech Matusik (MIT CSAIL); Eitan Grinspun (University of Toro
 nto); Kevin Carlberg (Meta Reality Labs Research); Chenfanfu Jiang (Univer
 sity of California Los Angeles); and Peter Yichen Chen (MIT CSAIL)\n\nRegi
 stration Category: Full Access\n\nSession Chair: Tao Du (Tsinghua Universi
 ty, Shanghai Qi Zhi Institute)
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