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  PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time

Shimada, S., Golyanik, V., Xu, W., & Theobalt, C. (2020). PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time. Retrieved from https://arxiv.org/abs/2008.08880.

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Latex : {PhysCap}: {P}hysically Plausible Monocular {3D} Motion Capture in Real Time

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 Creators:
Shimada, Soshi1, Author           
Golyanik, Vladislav1, Author           
Xu, Weipeng1, Author           
Theobalt, Christian1, Author                 
Affiliations:
1Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              

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Free keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Graphics, cs.GR
 Abstract: Marker-less 3D human motion capture from a single colour camera has seen
significant progress. However, it is a very challenging and severely ill-posed
problem. In consequence, even the most accurate state-of-the-art approaches
have significant limitations. Purely kinematic formulations on the basis of
individual joints or skeletons, and the frequent frame-wise reconstruction in
state-of-the-art methods greatly limit 3D accuracy and temporal stability
compared to multi-view or marker-based motion capture. Further, captured 3D
poses are often physically incorrect and biomechanically implausible, or
exhibit implausible environment interactions (floor penetration, foot skating,
unnatural body leaning and strong shifting in depth), which is problematic for
any use case in computer graphics. We, therefore, present PhysCap, the first
algorithm for physically plausible, real-time and marker-less human 3D motion
capture with a single colour camera at 25 fps. Our algorithm first captures 3D
human poses purely kinematically. To this end, a CNN infers 2D and 3D joint
positions, and subsequently, an inverse kinematics step finds space-time
coherent joint angles and global 3D pose. Next, these kinematic reconstructions
are used as constraints in a real-time physics-based pose optimiser that
accounts for environment constraints (e.g., collision handling and floor
placement), gravity, and biophysical plausibility of human postures. Our
approach employs a combination of ground reaction force and residual force for
plausible root control, and uses a trained neural network to detect foot
contact events in images. Our method captures physically plausible and
temporally stable global 3D human motion, without physically implausible
postures, floor penetrations or foot skating, from video in real time and in
general scenes. The video is available at
http://gvv.mpi-inf.mpg.de/projects/PhysCap

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Language(s): eng - English
 Dates: 2020-08-202020-12-092020
 Publication Status: Published online
 Pages: 16 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2008.08880
BibTex Citekey: Shimada_2008.08880
URI: https://arxiv.org/abs/2008.08880
 Degree: -

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