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DTSTAMP:20240214T070242Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T144000
DTEND;TZID=Australia/Melbourne:20231212T145000
UID:siggraphasia_SIGGRAPH Asia 2023_sess120_papers_178@linklings.com
SUMMARY:MuscleVAE: Model-Based Controllers of Muscle-Actuated Characters
DESCRIPTION:Technical Papers\n\nYusen Feng, Xiyan Xu, and Libin Liu (Pekin
 g University)\n\nIn this paper, we present a simulation and control framew
 ork for generating biomechanically plausible motion for muscle-actuated ch
 aracters. We incorporate a fatigue dynamics model, the 3CC-r model, into t
 he widely-adopted Hill-type muscle model to simulate the development and r
 ecovery of fatigue in muscles, which creates a natural evolution of motion
  style caused by the accumulation of fatigue from prolonged activities. To
  address the challenging problem of controlling a musculoskeletal system w
 ith high degrees of freedom, we propose a novel muscle-space control strat
 egy based on PD control. Our simulation and control framework facilitates 
 the training of a generative model for muscle-based motion control, which 
 we refer to as MuscleVAE. By leveraging the variational autoencoders (VAEs
 ), MuscleVAE is capable of learning a rich and flexible latent representat
 ion of skills from a large unstructured motion dataset, encoding not only 
 motion features but also muscle control and fatigue properties. We demonst
 rate that the MuscleVAE model can be efficiently trained using a model-bas
 ed approach, resulting in the production of high-fidelity motions and enab
 ling a variety of downstream tasks.\n\nRegistration Category: Full Access\
 n\nSession Chair: Jungdam Won (Seoul National University)
URL:https://asia.siggraph.org/2023/full-program?id=papers_178&sess=sess120
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