Seeing through the appearance: Body shape estimation using multi-view clothing images

We propose a learning-based algorithm for body shape estimation, which only requires 2D clothing images taken in multiple views as the input data. Compared with the use of 3D scanners or depth cameras, although our setting is more user friendly, it also makes the learning and estimation problems more challenging. In addition to utilizing ground truth body images for constructing human body models at each view of interest, our work uniquely associates the anthropometric measurements (e.g., body height or leg length) across different views. For performing body shape estimation using multi-view clothing images, the proposed algorithm solves an optimization task which recovers the body shape with image and measurement reconstruction guarantees. In the experiments, we will show that the use of our proposed method would achieve satisfactory estimation results, and performs favorably against single-view or other baseline approaches for both body shape and measurement estimation.