Anthropometric Parameter Measurement from Equivariant Multi-view Images

Abstract

In this paper, we propose an anthropometric parameter measurement method that any customized parameter can be measured online by the pre-selected endpoints on the reconstructed 3D body models of equivariant multi-view images. The method includes 3D body model reconstruction, anthropometric parameter measurement, and parameter modification. In 3D body model reconstruction, we detect and segment the human body from its background and reconstruct a generative 3D body model from the segmented image with deep learning. And then we measure anthropometric parameter on the reconstructed 3D body model of each view. Before parameter measurement, we manually pre-select endpoints associated with all anthropometric parameters on the reconstructed 3D body model since all vertices of the reconstructed body model are ordered. However, the information of a single-view image is insufficient and the measurement result is varied regularly by the view changes. To improve the measurement accuracy, we design a convolutional neural network in the last step which can regress more accurate anthropometric parameters from equivariant multi-view measurements. Experimental results on the representative dataset demonstrate that the proposed method can measure planar and spatial anthropometric parameters automatically with comparable performance.