The understanding of body measurements in and between populations is important and has many applications in medicine surveying, the fashion industry, fitness, and entertainment. Recent advances in human body measurement and shape estimation have been significantly driven by statistical models and deep learning, enabling methods that estimate 3D human meshes from 3D point clouds and 2D images - so called mesh regression methods. This thesis builds upon the state-of-the-art mesh regression approaches from multiple images. The first step is to propose the simplest method and use it as a baseline. The baseline is a linear regression models that takes only person's self-estimated height and weight and estimates the corresponding mesh. The baseline performs surprisingly well compared to the state-of-the-art methods. The second contribution is a 3D human pose estimation model from multiple camera views. The novelty of the model is in the fact that it can take any set of camera views as input, regardless of their relative arrangement and the number of cameras. The third contribution is a model for estimating the parameters of human pose, shape, and clothes from a single image. The estimated parameters are interpretable and, thus, controllable, which is a significant advantage compared to the previous approaches and important for many anthropometric applications. The three proposed models are evaluated in details and compared to the state-of-the-art methods.