  The estimation of 3D human body shape and clothing measurements is crucial
for virtual try-on and size recommendation problems in the fashion industry but
has always been a challenging problem due to several conditions, such as lack
of publicly available realistic datasets, ambiguity in multiple camera
resolutions, and the undefinable human shape space. Existing works proposed
various solutions to these problems but could not succeed in the industry
adaptation because of complexity and restrictions. To solve the complexity and
challenges, in this paper, we propose a simple yet effective architecture to
estimate both shape and measures from frontal- and side-view images. We utilize
silhouette segmentation from the two multi-view images and implement an
auto-encoder network to learn low-dimensional features from segmented
silhouettes. Then, we adopt a kernel-based regularized regression module to
estimate the body shape and measurements. The experimental results show that
the proposed method provides competitive results on the synthetic dataset,
NOMO-3d-400-scans Dataset, and RGB Images of humans captured in different
cameras.
