  The recent advances in 3-D imaging technologies give rise to databases of
human shapes, from which statistical shape models can be built. These
statistical models represent prior knowledge of the human shape and enable us
to solve shape reconstruction problems from partial information. Generating
human shape from traditional anthropometric measurements is such a problem,
since these 1-D measurements encode 3-D shape information. Combined with a
statistical shape model, these easy-to-obtain measurements can be leveraged to
create 3D human shapes. However, existing methods limit the creation of the
shapes to the space spanned by the database and thus require a large amount of
training data. In this paper, we introduce a technique that extrapolates the
statistically inferred shape to fit the measurement data using nonlinear
optimization. This method ensures that the generated shape is both human-like
and satisfies the measurement conditions. We demonstrate the effectiveness of
the method and compare it to existing approaches through extensive experiments,
using both synthetic data and real human measurements.
