  This research proposes a novel adjustable algorithm for reconstructing 3D
body shapes from front and side silhouettes. Most recent silhouette-based
approaches use a deep neural network trained by silhouettes and key points to
estimate the shape parameters but cannot accurately fit the model to the body
contours and consequently are struggling to cover detailed body geometry,
especially in the torso. In addition, in most of these cases, body parts have
the same accuracy priority, making the optimization harder and avoiding
reaching the optimum possible result in essential body parts, like the torso,
which is visually important in most applications, such as virtual garment
fitting. In the proposed method, we can adjust the expected accuracy for each
body part based on our purpose by assigning coefficients for the distance of
each body part between the projected 3D body and 2D silhouettes. To measure
this distance, we first recognize the correspondent body parts using body
segmentation in both views. Then, we align individual body parts by 2D rigid
registration and match them using pairwise matching. The objective function
tries to minimize the distance cost for the individual body parts in both views
based on distances and coefficients by optimizing the statistical model
parameters. We also handle the slight variation in the degree of arms and limbs
by matching the pose. We evaluate the proposed method with synthetic body
meshes from the normalized S-SCAPE. The result shows that the algorithm can
more accurately reconstruct visually important body parts with high
coefficients.
