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Abstract:
We present a novel algorithm to jointly capture the motion and the dynamic
shape of humans from
multiple video streams without using optical markers. Instead of relying on
kinematic skeletons,
as traditional motion capture methods, our approach uses a deformable
high-quality mesh of a human
as scene representation. It jointly uses an image-based
\mbox{3D} correspondence estimation algorithm and a fast
Laplacian mesh deformation scheme to capture both
motion and surface deformation
of the actor from the input video footage. As opposed to many related methods,
our algorithm can track people wearing wide apparel, it can straightforwardly
be applied to
any type of subject, e.g. animals, and it preserves the connectivity
of the mesh over time. We demonstrate the performance of our approach using
synthetic and
captured real-world video sequences and validate its accuracy by comparison to
the ground truth.
