There is high demand for online fashion recommender systems that incorporate
the needs of the consumer's body shape. As such, we present a methodology to
classify human body shape from a single image. This is achieved through the use
of instance segmentation and keypoint estimation models, trained only on
open-source benchmarking datasets. The system is capable of performing in noisy
environments owing to to robust background subtraction. The proposed
methodology does not require 3D body recreation as a result of classification
based on estimated keypoints, nor requires historical information about a user
to operate - calculating all required measurements at the point of use. We
evaluate our methodology both qualitatively against existing body shape
classifiers and quantitatively against a novel dataset of images, which we
provide for use to the community. The resultant body shape classification can
be utilised in a variety of downstream tasks, such as input to size and fit
recommendation or virtual try-on systems.

本文提出了一个从单一图像中分类人体形状的方法，该系统不需要 3D 身体重建，可以在嘈杂的环境中进行操作，可以用于推荐合适的尺寸和试穿系统。