  This paper presents a novel method for 3D human pose and shape estimation
from images with sparse views, using joint points and silhouettes, based on a
parametric model. Firstly, the parametric model is fitted to the joint points
estimated by deep learning-based human pose estimation. Then, we extract the
correspondence between the parametric model of pose fitting and silhouettes on
2D and 3D space. A novel energy function based on the correspondence is built
and minimized to fit parametric model to the silhouettes. Our approach uses
sufficient shape information because the energy function of silhouettes is
built from both 2D and 3D space. This also means that our method only needs
images from sparse views, which balances data used and the required prior
information. Results on synthetic data and real data demonstrate the
competitive performance of our approach on pose and shape estimation of the
human body.
