Section:
New Results
Building Statistical Shape
Spaces for 3D Human Modeling
Statistical models of 3D human shape and pose learned from scan
databases have developed into valuable tools to solve a variety of
vision and graphics problems. Unfortunately, most publicly available
models are of limited expressiveness as they were learned on very
small databases that hardly reflect the true variety in human body
shapes. In this paper, we contribute by rebuilding a widely used
statistical body representation from the largest commercially
available scan database, and making the resulting model available to
the community (visit http://humanshape.mpi-inf.mpg.de ). As
preprocessing several thousand scans for learning the model is a
challenge in itself, we contribute by developing robust best
practice solutions for scan alignment that quantitatively lead to
the best learned models. We make implementations of these
preprocessing steps also publicly available. We extensively evaluate
the improved accuracy and generality of our new model, and show its
improved performance for human body reconstruction from sparse input
data. This work was published as Max Planck research
report [17] .
Figure
6. Visualization of the first three principal components learned from a large database of posture-normalized 3D human body scans [17] .
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