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キーワード:
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要旨:
This paper proposes a method for capturing the performance of a human or
an animal from a multi-view video sequence. Given an articulated
template model and silhouettes from a multi-view image sequence, our
approach recovers not only the movement of the skeleton, but also the
possibly non-rigid temporal deformation of the 3D surface.
While large scale deformations or fast movements are captured by the skeleton
pose and approximate surface skinning, true small scale deformations or
non-rigid garment motion are captured by fitting the surface to
the silhouette. We further
propose a novel optimization scheme for skeleton-based pose estimation
that exploits the skeleton's tree structure to split the
optimization problem into a local one and a lower dimensional global one.
We show on various sequences that our approach can capture the 3D motion of
animals and humans accurately even in the case of rapid movements and
wide apparel like skirts.
