Human Body Shape Classification Based on a Single Image
Cameron Trotter Filipa Peleja Dario Dotti Alberto de Santos
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.