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DTSTAMP:20260921T150443Z
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:Motion Control via Metric-Aligning Motion Matching\n\nMetric-A
 ligning Motion Matching (MAMM) is a novel method for controlling motion se
 quences using sketches, labels, audio, or another motion sequence without 
 requiring training or annotations. By aligning within-domain distances, MA
 MM provides a flexible and efficient solution for motion control acros...\
 n\n\nNaoki Agata and Takeo Igarashi (The University of Tokyo)\n-----------
 ----------\nAutoKeyframe: Autoregressive Keyframe Generation for Human Mot
 ion Synthesis and Editing\n\nWe present AutoKeyframe, a novel framework th
 at simultaneously accepts dense and sparse control signals for motion gene
 ration by generating keyframes directly. Our method reduces manual efforts
  for keyframing while maintaining precise controllability, using an autore
 gressive diffusion model and a ne...\n\n\nBowen Zheng and Ke Chen (Zhejian
 g 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); He Wang (U
 CL Centre for Artificial Intelligence, Department of Computer Science, Uni
 versity College London); Joan Lasenby (University of Cambridge, Department
  of Engineering); and Xiaogang Jin (Zhejiang University; State Key Laborat
 ory of CAD&CG, Zhejiang University)\n---------------------\nKinematic Moti
 on Retargeting for Contact-Rich Anthropomorphic Manipulations\n\nWe presen
 t a simple, but effective framework for kinematically retargeting contact-
 rich anthropomorphic hand-object manipulations by exploiting contact areas
 . We reliably retarget contact area data between diverse hands using a nov
 el non-isometric shape matching process and generate high quality res...\n
 \n\nArjun Lakshmipathy, Jessica Hodgins, and Nancy Pollard (Carnegie Mello
 n University) and Arjun Lakshmipathy\n---------------------\nAnyTop: Chara
 cter Animation Diffusion with Any Topology\n\nAnyTop generates motion for 
 diverse character skeletons using only skeletal structure as input. This d
 iffusion model incorporates topology information and textual joint descrip
 tions to learn semantic correspondences across different skeletons. It gen
 eralizes with minimal training examples and suppor...\n\n\nInbar Gat, Siga
 l Raab, Guy Tevet, Yuval Reshef, Amit Haim Bermano, and Daniel Cohen-Or (T
 el Aviv University)\n---------------------\nAdvancing GPU IPC for Stiff Af
 fine-Deformable Simulation\n\nWe present a GPU-optimized IPC framework ach
 ieving up to 10× speedup across soft, stiff, and hybrid simulations. Key i
 nnovations include a connectivity-enhanced MAS preconditioner, a parallel-
 friendly inexact strain limiting energy, and a hash-based two-level reduct
 ion strategy for fast Hes-\nsian as...\n\n\nKemeng Huang (Carnegie Mellon 
 University, The University of Hong Kong); Xinyu Lu (TransGP); Huancheng Li
 n (Carnegie Mellon University, The University of Hong Kong); Taku Komura (
 The University of Hong Kong); Minchen Li (Carnegie Mellon University); and
  Kemeng Huang\n---------------------\nJGS2: Near Second-order Converging J
 acobi/Gauss-Seidel for GPU Elastodynamics\n\nThis paper presents a new GPU
  simulation algorithm, which converges as fast as global Newton's method a
 nd 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 (Universi
 ty of Utah); Weiwei Xu (State Key Lab of CAD&CG, Zhejiang University, Chin
 a); Hao Su (UCSD); Huamin Wang (Style3D Research); Chenfanfu Jiang (UCLA);
  and Yin Yang (University of Utah)\n---------------------\nHyper-Dimension
 al Deformation Simulation\n\nEver feel like three dimensions isn't quite e
 nough? We performed the analysis necessary to simulate the motion of defor
 mables in four spatial dimensions! Along the way, we developed techniques 
 for generating simulation-ready hyper-meshes, analyzing hyper-dimensional 
 deformation energies, and detecti...\n\n\nAlvin Shi, Haomiao Wu, and Theod
 ore Kim (Yale University)\n---------------------\nAll About Motion & Defor
 mation - Interactive Discussion\n\nAfter the summary presentations, attend
 ees will participate in an interactive discussion. Outside the room will b
 e a series of poster boards for authors to gather around with the audience
 . Authors are invited to bring any material related to their paper that co
 uld instigate further conversation such...\n\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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