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Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
Abstract:
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.
