During the past few years we have witnessed the development
of many methodologies for building and fitting Statistical
Deformable Models (SDMs). The construction of
accurate SDMs requires careful annotation of images with
regards to a consistent set of landmarks. However, the manual
annotation of a large amount of images is a tedious,
laborious and expensive procedure. Furthermore, for several
deformable objects, e.g. human body, it is difficult to
define a consistent set of landmarks, and, thus, it becomes
impossible to train humans in order to accurately annotate
a collection of images. Nevertheless, for the majority of
objects, it is possible to extract the shape by object segmentation
or even by shape drawing. In this paper, we show for
the first time, to the best of our knowledge, that it is possible
to construct SDMs by putting object shapes in dense
correspondence. Such SDMs can be built with much less
effort for a large battery of objects. Additionally, we show
that, by sampling the dense model, a part-based SDM can
be learned with its parts being in correspondence. We employ
our framework to develop SDMs of human arms and
legs, which can be used for the segmentation of the outline
of the human body, as well as to provide better and more
consistent annotations for body joints.