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DTSTAMP:20260114T163748Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T140000
DTEND;TZID=Australia/Melbourne:20231212T151500
UID:siggraphasia_SIGGRAPH Asia 2023_sess120@linklings.com
SUMMARY:Character and Rigid Body Control
DESCRIPTION:AdaptNet: Policy Adaptation for Physics-Based Character Contro
 l\n\nMotivated by human’s ability to adapt skills in the learning of new o
 nes, this paper presents AdaptNet, an approach for modifying the latent sp
 ace of existing policies to allow new behaviors to be quickly learned from
  like tasks in comparison to learning from scratch. Building on top of a g
 ive...\n\n\nPei Xu (Clemson University, Roblox); Kaixiang Xie (McGill Univ
 ersity); Sheldon Andrews (École de technologie supérieure, Roblox); Paul G
 . Kry (McGill University); Michael Neff (University of California Davis); 
 Morgan McGuire (Roblox, University of Waterloo); Ioannis Karamouzas (Unive
 rsity of California Riverside); and Victor Zordan (Roblox, Clemson Univers
 ity)\n---------------------\nMuscleVAE: Model-Based Controllers of Muscle-
 Actuated Characters\n\nIn this paper, we present a simulation and control 
 framework for generating biomechanically plausible motion for muscle-actua
 ted characters. We incorporate a fatigue dynamics model, the 3CC-r model, 
 into the widely-adopted Hill-type muscle model to simulate the development
  and recovery of fatigue in...\n\n\nYusen Feng, Xiyan Xu, and Libin Liu (P
 eking University)\n---------------------\nC·ASE: Learning Conditional Adve
 rsarial Skill Embeddings for Physics-based Characters\n\nWe present C·ASE,
  an efficient and effective framework that learns conditional Adversarial 
 Skill Embeddings for physics-based characters. Our physically simulated ch
 aracter can learn a diverse repertoire of skills while providing controlla
 bility in the form of direct manipulation of the skills to be...\n\n\nZhiy
 ang Dou (The University of Hong Kong (HKU)), Xuelin Chen and Qingnan Fan (
 Tencent AI Lab), Taku Komura (University of Hong Kong), and Wenping Wang (
 Texas A&M University)\n---------------------\nViCMA: Visual Control of Mul
 tibody Animations\n\nMotion control of large-scale, multibody physics anim
 ations with contact is difficult. Existing approaches, such as those based
  on optimization, are computationally daunting, and, as the number of inte
 racting objects increases, can fail to find satisfactory solutions. We pre
 sent a new, complementary...\n\n\nDoug L. James (Stanford University, NVID
 IA) and David I. W. Levin (University of Toronto, NVIDIA)\n---------------
 ------\nNeural Categorical Priors for Physics-Based Character Control\n\nR
 ecent advances in learning reusable motion priors have demonstrated their 
 effectiveness in generating naturalistic behaviors. In this paper, we prop
 ose a new learning framework in this paradigm for controlling physics-base
 d characters with significantly improved motion quality and diversity over
  ex...\n\n\nQingxu Zhu, He Zhang, Mengting Lan, and Lei Han (Tencent)\n---
 ------------------\nDiffFR: Differentiable SPH-based Fluid-Rigid Coupling 
 for Rigid Body Control\n\nDifferentiable physics simulation has shown its 
 efficacy in inverse design problems. Given the pervasiveness of the divers
 e interactions between fluids and solids in life, a differentiable simulat
 or for the inverse design of the motion of rigid objects in two-way fluid-
 rigid coupling is also demande...\n\n\nZhehao Li and Qingyu Xu (University
  of Science and Technology of China), Xiaohan Ye and Bo Ren (Nankai Univer
 sity), and Ligang Liu (University of Science and Technology of China)\n\nR
 egistration Category: Full Access\n\nSession Chair: Jungdam Won (Seoul Nat
 ional University)
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