  We propose an end-to-end deep learning architecture that produces a 3D shape
in triangular mesh from a single color image. Limited by the nature of deep
neural network, previous methods usually represent a 3D shape in volume or
point cloud, and it is non-trivial to convert them to the more ready-to-use
mesh model. Unlike the existing methods, our network represents 3D mesh in a
graph-based convolutional neural network and produces correct geometry by
progressively deforming an ellipsoid, leveraging perceptual features extracted
from the input image. We adopt a coarse-to-fine strategy to make the whole
deformation procedure stable, and define various of mesh related losses to
capture properties of different levels to guarantee visually appealing and
physically accurate 3D geometry. Extensive experiments show that our method not
only qualitatively produces mesh model with better details, but also achieves
higher 3D shape estimation accuracy compared to the state-of-the-art.
