Shape Estimation of 3D Human Body Model Based on the Geometric Generation Model

Abstract

In this paper, we propose a geometric generation model for the shape estimation of 3D human body. The model decouples the two processes: (1) Encoding/decoding process maps the samples between the image space X and the latent (feature) space Z. This step achieves the dimension deduction. (2) The process of the Probability measure transformation transforms a fixed distribution ζ G P(Z) to any given distribution µ G P(Z). Before the shape estimation, U-V transform is done with Densepose, in order to transform the 2D human body image to 3D shape pose image.