  Recent advances in consumer depth sensors have created many opportunities for
human body measurement and modeling. Estimation of 3D body shape is
particularly useful for fashion e-commerce applications such as virtual try-on
or fit personalization. In this paper, we propose a method for capturing
accurate human body shape and anthropometrics from a single consumer grade
depth sensor. We first generate a large dataset of synthetic 3D human body
models using real-world body size distributions. Next, we estimate key body
measurements from a single monocular depth image. We combine body measurement
estimates with local geometry features around key joint positions to form a
robust multi-dimensional feature vector. This allows us to conduct a fast
nearest-neighbor search to every sample in the dataset and return the closest
one. Compared to existing methods, our approach is able to predict accurate
full body parameters from a partial view using measurement parameters learned
from the synthetic dataset. Furthermore, our system is capable of generating 3D
human mesh models in real-time, which is significantly faster than methods
which attempt to model shape and pose deformations. To validate the efficiency
and applicability of our system, we collected a dataset that contains frontal
and back scans of 83 clothed people with ground truth height and weight.
Experiments on real-world dataset show that the proposed method can achieve
real-time performance with competing results achieving an average error of 1.9
cm in estimated measurements.
