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DTSTAMP:20260417T190052Z
LOCATION:West Building\, Rooms 220-222
DTSTART;TZID=America/Los_Angeles:20250811T104500
DTEND;TZID=America/Los_Angeles:20250811T123500
UID:siggraph_SIGGRAPH 2025_sess143@linklings.com
SUMMARY:All About Motion & Deformation
DESCRIPTION:Hyper-Dimensional Deformation Simulation\n\nEver feel like thr
 ee dimensions isn't quite enough? We performed the analysis necessary to s
 imulate the motion of deformables in four spatial dimensions! Along the wa
 y, we developed techniques for generating simulation-ready hyper-meshes, a
 nalyzing hyper-dimensional deformation energies, and detecti...\n\n\nAlvin
  Shi, Haomiao Wu, and Theodore Kim (Yale University)\n--------------------
 -\nAll About Motion & Deformation - Interactive Discussion\n\nAfter the su
 mmary presentations, attendees will participate in an interactive discussi
 on. Outside the room will be a series of poster boards for authors to gath
 er around with the audience. Authors are invited to bring any material rel
 ated to their paper that could instigate further conversation such...\n\n-
 --------------------\nMotion Control via Metric-Aligning Motion Matching\n
 \nMetric-Aligning Motion Matching (MAMM) is a novel method for controlling
  motion sequences using sketches, labels, audio, or another motion sequenc
 e without requiring training or annotations. By aligning within-domain dis
 tances, MAMM provides a flexible and efficient solution for motion control
  acros...\n\n\nNaoki Agata and Takeo Igarashi (The University of Tokyo)\n-
 --------------------\nKinematic Motion Retargeting for Contact-Rich Anthro
 pomorphic Manipulations\n\nWe present a simple, but effective framework fo
 r kinematically retargeting contact-rich anthropomorphic hand-object manip
 ulations by exploiting contact areas. We reliably retarget contact area da
 ta between diverse hands using a novel non-isometric shape matching proces
 s and generate high quality res...\n\n\nArjun Lakshmipathy, Jessica Hodgin
 s, and Nancy Pollard (Carnegie Mellon University) and Arjun Lakshmipathy\n
 ---------------------\nAnyTop: Character Animation Diffusion with Any Topo
 logy\n\nAnyTop generates motion for diverse character skeletons using only
  skeletal structure as input. This diffusion model incorporates topology i
 nformation and textual joint descriptions to learn semantic correspondence
 s across different skeletons. It generalizes with minimal training example
 s and suppor...\n\n\nInbar Gat, Sigal Raab, Guy Tevet, Yuval Reshef, Amit 
 Haim Bermano, and Daniel Cohen-Or (Tel Aviv University)\n-----------------
 ----\nAdvancing GPU IPC for Stiff Affine-Deformable Simulation\n\nWe prese
 nt a GPU-optimized IPC framework achieving up to 10× speedup across soft, 
 stiff, and hybrid simulations. Key innovations include a connectivity-enha
 nced MAS preconditioner, a parallel-friendly inexact strain limiting energ
 y, and a hash-based two-level reduction strategy for fast Hes-\nsian as...
 \n\n\nKemeng Huang (Carnegie Mellon University, The University of Hong Kon
 g); Xinyu Lu (TransGP); Huancheng Lin (Carnegie Mellon University, The Uni
 versity of Hong Kong); Taku Komura (The University of Hong Kong); Minchen 
 Li (Carnegie Mellon University); and Kemeng Huang\n---------------------\n
 JGS2: Near Second-order Converging Jacobi/Gauss-Seidel for GPU Elastodynam
 ics\n\nThis paper presents a new GPU simulation algorithm, which converges
  as fast as global Newton's method and as efficient as Jacobi method.\n\n\
 nLei Lan (University of Utah; State Key Lab of CAD&CG, Zhejiang University
 ); Zixuan Lu and Chun Yuan (University of Utah); Weiwei Xu (State Key Lab 
 of CAD&CG, Zhejiang University, China); Hao Su (UCSD); Huamin Wang (Style3
 D Research); Chenfanfu Jiang (UCLA); and Yin Yang (University of Utah)\n--
 -------------------\nAutoKeyframe: Autoregressive Keyframe Generation for 
 Human Motion Synthesis and Editing\n\nWe present AutoKeyframe, a novel fra
 mework that simultaneously accepts dense and sparse control signals for mo
 tion generation by generating keyframes directly. Our method reduces manua
 l efforts for keyframing while maintaining precise controllability, using 
 an autoregressive diffusion model and a ne...\n\n\nBowen Zheng and Ke Chen
  (Zhejiang University; State Key Laboratory of CAD&CG, Zhejiang University
 ); Yuxin Yao (University of Cambridge, Department of Engineering); Zijiao 
 Zeng and Xinwei Jiang (Tencent Games Digital Content Technology Center); H
 e Wang (UCL Centre for Artificial Intelligence, Department of Computer Sci
 ence, University College London); Joan Lasenby (University of Cambridge, D
 epartment of Engineering); and Xiaogang Jin (Zhejiang University; State Ke
 y Laboratory of CAD&CG, Zhejiang University)\n\nInterest Area: Research & 
 Education\n\nRecording: Livestreamed, Not Livestreamed, Recorded, Not Reco
 rded\n\nRegistration Category: Full Conference, Virtual Access, Monday\n\n
 Session Chair: David Levin (University of Toronto, NVIDIA)
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