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DTSTAMP:20240214T070250Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231215T154000
DTEND;TZID=Australia/Melbourne:20231215T155000
UID:siggraphasia_SIGGRAPH Asia 2023_sess139_papers_403@linklings.com
SUMMARY:Efficient Human Motion Reconstruction from Monocular Videos with P
 hysical Consistency Loss
DESCRIPTION:Technical Papers\n\nLin Cong and Philipp Ruppel (Universität H
 amburg), Yizhou Wang (Peking University), Xiang Pan (Diago Tech Company), 
 and Norman Hendrich and Jianwei Zhang (Universität Hamburg)\n\nVision-only
  motion reconstruction from monocular videos often produces artifacts such
  as foot sliding and jittery motions. Existing physics-based methods typic
 ally either simplify the problem to focus solely on feet-ground contacts, 
 or reconstruct full-body contacts within a physics simulator, necessitatin
 g the solution of a time-consuming bilevel optimization problem. \nTo over
 come these limitations, we present an efficient gradient-based method for 
 reconstructing complex human motions (including highly dynamic and acrobat
 ic movements) with physical constraints. Our approach reformulates human m
 otion dynamics through a differentiable physical consistency loss within a
 n augmented search space that accounts both for contacts and the camera al
 ignment. This enables us to transform the motion reconstruction task into 
 a single-level trajectory optimization problem. Experimental results demon
 strate that our method can reconstruct complex human motions from real-wor
 ld videos in minutes, which is substantially faster than previous approach
 es. Additionally, the reconstructed results show enhanced physical realism
  compared to existing methods.\n\nRegistration Category: Full Access\n\nSe
 ssion Chair: Yuting Ye (Reality Labs Research, Meta)
URL:https://asia.siggraph.org/2023/full-program?id=papers_403&sess=sess139
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